Energy consumption test method, device, equipment and storage medium

Abstract

The present application discloses an energy consumption test method, device, equipment and storage medium, which are used to solve the problem of inaccurate energy consumption test of vehicles in related technologies. In an embodiment of the present application, the acquired vehicle data of the target vehicle model is split based on the preset driving speed to obtain at least one data segment; at least one data segment is classified according to the preset calibration speed to obtain at least one speed interval; the first characteristic value of each speed interval is determined; the target data segment is determined from the speed interval based on the first characteristic value; simulation data is constructed based on the target data segment; and the energy consumption data of the target vehicle model is obtained based on the simulation data and a pre-trained energy consumption test model. In the present application, the simulation data covers vehicle data of different speed intervals, so that the simulation data is more similar to the actual situation of the vehicle driving in the city, making the simulation data more representative, and ensuring the accuracy of the obtained energy consumption data.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to an energy consumption testing method, device, equipment and storage medium. Background Art

[0002] With the development of society, the number of vehicles is gradually increasing. Users are particularly concerned about the energy consumption of vehicles when purchasing vehicles. In related technologies, the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) test method and the China Light-duty Vehicle Test cycle (CLTC) are usually used to test the energy consumption of vehicles. However, the WLTC / CLTC test method conditions cannot fully represent the driving styles and driving scenario differences of different groups of people, resulting in a large deviation between the actual fuel consumption of some users and the test results. Therefore, these scenarios are ignored when using the WLTC / CLTC conditions for calibration and development, resulting in poor energy consumption performance when the vehicle is actually used. Summary of the invention

[0003] In view of this, the present application provides an energy consumption test method, device, equipment and storage medium to help solve the problem of inaccurate energy consumption testing of vehicles in related technologies.

[0004] In a first aspect, an embodiment of the present application provides an energy consumption test method, the method comprising:

[0005] Splitting the acquired vehicle data of the target vehicle model based on a preset driving speed to obtain at least one data segment;

[0006] Classifying the at least one data segment according to the preset calibration speed to obtain at least one speed interval;

[0007] For each of the at least one speed interval, based on the vehicle data of the data segment included in the speed interval, obtain a first characteristic value of the speed interval; based on the first characteristic value, determine a target data segment from the speed interval;

[0008] constructing simulation data based on the target data segment;

[0009] The energy consumption data of the target vehicle model is obtained based on the simulation data and a pre-trained energy consumption test model.

[0010] In the present application, by setting up classification of data segments obtained by splitting vehicle data, and determining the target data segments for constructing simulation data based on each speed range, the simulation data covers vehicle data in different speed ranges, making the simulation data more similar to the actual situation of vehicle driving in the city, making the simulation data more representative and universal, and obtaining energy consumption data based on a preset energy consumption test model, the accuracy of the obtained energy consumption data is guaranteed.

[0011] In some possible embodiments, obtaining the first characteristic value of the speed interval based on the vehicle data of the data segments included in the speed interval includes:

[0012] A first characteristic value of the speed interval is obtained based on the vehicle data of each data segment included in the speed interval and a preset algorithm.

[0013] In the present application, a preset algorithm is used to obtain the characteristic value of the speed interval, so that the characteristic value can represent the overall level of the vehicle data in the speed interval.

[0014] In some possible embodiments, before obtaining the first characteristic value of the speed interval based on the vehicle data of the data segments included in the speed interval, the method further includes:

[0015] For each data segment in the speed interval, determining whether the positioning data corresponding to the data segment is within a positioning data range; wherein the positioning data range is determined based on a preset calibration speed corresponding to the speed interval;

[0016] If it is determined that the positioning data corresponding to the data segment is not within the positioning data range, the data segment is removed from the speed interval.

[0017] In the present application, data segments in the speed interval are screened based on the positioning data and the positioning data range, thereby avoiding the problem of inaccurate energy consumption data resulting from abnormal data segments participating in subsequent calculations.

[0018] In some possible embodiments, determining a target data segment from the speed interval based on the first characteristic value includes:

[0019] Selecting a first number of data segments from the speed interval according to a preset rule;

[0020] The first process is cyclically executed for the first number of data segments until a target data segment is determined; wherein the first process includes:

[0021] determining second characteristic values ​​of the first number of data segments based on the vehicle data of the first number of data segments;

[0022] determining whether a first difference between the second eigenvalue and the first eigenvalue is less than a first difference threshold;

[0023] If it is determined that the first difference is less than the first difference threshold, taking the first number of data segments as target data segments;

[0024] If it is determined that the first difference is not less than the first difference threshold, determining whether the second eigenvalue is greater than the first eigenvalue;

[0025] If it is determined that the second characteristic value is greater than the first characteristic value, the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments; wherein the first data segment is any one of the data segments in the first number of data segments whose vehicle data is greater than the second characteristic value, and the second data segment is any one of the data segments in the speed range whose vehicle data is less than the first data segment;

[0026] If it is determined that the second eigenvalue is smaller than the first eigenvalue, the third data segment is eliminated from the first number of data segments, and the fourth data segment is used to update the first number of data segments; wherein the third data segment is any one of the data segments in the first number of data segments whose vehicle data is smaller than the second eigenvalue, and the fourth data segment is any one of the data segments in the speed range whose vehicle data is larger than the third data segment.

[0027] In the present application, the target data segment is determined by cyclically executing the first process, so that the determined target data segment can accurately represent the average level of vehicle data in the speed range, further ensuring the accuracy of the obtained energy consumption data.

[0028] In some possible embodiments, determining the second characteristic values ​​of the first number of data segments based on the vehicle data of the first number of data segments includes:

[0029] The second characteristic value is obtained based on the vehicle data of the first number of data segments and a preset algorithm.

[0030] In the present application, a preset algorithm is used to obtain the characteristic value of the speed interval, so that the characteristic value can represent the overall level of the vehicle data in the speed interval.

[0031] In some possible embodiments, constructing simulation data based on the target data segment includes:

[0032] According to a preset extraction rule, a preset number of data segments are extracted from the target data segments corresponding to each speed interval to construct a final data segment;

[0033] The simulation data is obtained based on the final data segment corresponding to each speed interval.

[0034] In the present application, the final data segment is constructed by extracting data segments from each speed range, so that the determined simulation data can cover different driving conditions of the vehicle, further ensuring the accuracy of the obtained energy consumption data.

[0035] In some possible embodiments, constructing simulation data based on the target data segment includes:

[0036] Obtaining a third characteristic value based on the acquired vehicle data of the target vehicle model;

[0037] Extracting a preset number of data segments from the target data segments corresponding to each speed interval as intermediate data segments according to a preset extraction rule;

[0038] Loopingly executing the second process based on the intermediate data segment and the third characteristic value until a final data segment is determined;

[0039] Obtaining the simulation data based on the final data segment;

[0040] The second process includes:

[0041] determining a fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment;

[0042] Determining whether a second difference between the third eigenvalue and the fourth eigenvalue is less than a second difference threshold;

[0043] If it is determined that the second difference is less than the second difference threshold, taking the intermediate data segment as the final data segment;

[0044] If it is determined that the second difference is not less than the second difference threshold, determining whether the fourth eigenvalue is greater than the third eigenvalue;

[0045] If it is determined that the fourth eigenvalue is greater than the third eigenvalue, the fifth data segment is removed from the intermediate data segment, and the sixth data segment is used to update the intermediate data segment; wherein the fifth data segment is any one of the data segments in the intermediate data segment whose vehicle data is greater than the fourth eigenvalue, and the sixth data segment is any one of the data segments in the speed interval to which the fifth data segment belongs whose vehicle data is less than the fifth data segment;

[0046] If it is determined that the fourth eigenvalue is smaller than the third eigenvalue, the seventh data segment is removed from the intermediate data segment, and the eighth data segment is used to update the intermediate data segment; wherein the seventh data segment is any one of the data segments in the intermediate data segment whose vehicle data is smaller than the fourth eigenvalue, and the eighth data segment is any one of the data segments in the speed range to which the seventh data segment belongs whose vehicle data is greater than the seventh data segment.

[0047] In the present application, the final data segment is determined by cyclically executing the second process, so that the determined final data segment can accurately represent the average level of vehicle data in the speed range, further ensuring the accuracy of the obtained energy consumption data.

[0048] In some possible embodiments, determining the fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment includes:

[0049] The fourth eigenvalue is obtained based on the vehicle data of the intermediate data segment and a preset algorithm.

[0050] In the present application, a preset algorithm is used to obtain the characteristic value of the speed interval, so that the characteristic value can represent the overall level of the vehicle data in the speed interval.

[0051] In some possible embodiments, the acquired vehicle data of the target vehicle model is split based on the preset driving speed to obtain at least one data segment, including:

[0052] determining a timestamp corresponding to when the preset driving speed appears in the vehicle data;

[0053] The vehicle data obtained between two time stamps that are adjacent in time series is regarded as a data segment.

[0054] In a second aspect, an embodiment of the present application provides an energy consumption testing device, the device comprising:

[0055] A splitting module, used to split the acquired vehicle data of the target vehicle model based on a preset driving speed to obtain at least one data segment;

[0056] A classification module, configured to classify the at least one data segment according to the preset calibration speed to obtain at least one speed interval;

[0057] a target data segment determination module, configured to obtain, for each of the at least one speed interval, a first characteristic value of the speed interval based on vehicle data of a data segment included in the speed interval; and determine a target data segment from the speed interval based on the first characteristic value;

[0058] A data construction module, used for constructing simulation data based on the target data segment;

[0059] The test module is used to obtain the energy consumption data of the target vehicle model based on the simulation data and a pre-trained energy consumption test model.

[0060] In some possible embodiments, when the target data segment determination module executes the vehicle data based on the data segments included in the speed interval to obtain the first characteristic value of the speed interval, it is specifically used to:

[0061] A first characteristic value of the speed interval is obtained based on the vehicle data of each data segment included in the speed interval and a preset algorithm.

[0062] In some possible embodiments, before the target data segment determination module executes the vehicle data based on the data segments included in the speed interval to obtain the first characteristic value of the speed interval, it is further configured to:

[0063] For each data segment in the speed interval, determining whether the positioning data corresponding to the data segment is within a positioning data range; wherein the positioning data range is determined based on a preset calibration speed corresponding to the speed interval;

[0064] If it is determined that the positioning data corresponding to the data segment is not within the positioning data range, the data segment is removed from the speed interval.

[0065] In some possible embodiments, when the target data segment determination module determines the target data segment from the speed interval based on the first feature value, it is specifically used to:

[0066] Selecting a first number of data segments from the speed interval according to a preset rule;

[0067] The first process is cyclically executed for the first number of data segments until a target data segment is determined; wherein the first process includes:

[0068] determining second characteristic values ​​of the first number of data segments based on the vehicle data of the first number of data segments;

[0069] determining whether a first difference between the second eigenvalue and the first eigenvalue is less than a first difference threshold;

[0070] If it is determined that the first difference is less than the first difference threshold, taking the first number of data segments as target data segments;

[0071] If it is determined that the first difference is not less than the first difference threshold, determining whether the second eigenvalue is greater than the first eigenvalue;

[0072] If it is determined that the second characteristic value is greater than the first characteristic value, the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments; wherein the first data segment is any one of the data segments in the first number of data segments whose vehicle data is greater than the second characteristic value, and the second data segment is any one of the data segments in the speed range whose vehicle data is less than the first data segment;

[0073] If it is determined that the second eigenvalue is smaller than the first eigenvalue, the third data segment is eliminated from the first number of data segments, and the fourth data segment is used to update the first number of data segments; wherein the third data segment is any one of the data segments in the first number of data segments whose vehicle data is smaller than the second eigenvalue, and the fourth data segment is any one of the data segments in the speed range whose vehicle data is larger than the third data segment.

[0074] In some possible embodiments, when the target data segment determination module determines the second characteristic values ​​of the first number of data segments based on the vehicle data of the first number of data segments, it is specifically used to:

[0075] The second characteristic value is obtained based on the vehicle data of the first number of data segments and a preset algorithm.

[0076] In some possible embodiments, when the data construction module executes construction of simulation data based on the target data segment, it is specifically used to:

[0077] According to a preset extraction rule, a preset number of data segments are extracted from the target data segments corresponding to each speed interval to construct a final data segment;

[0078] The simulation data is obtained based on the final data segment corresponding to each speed interval.

[0079] In some possible embodiments, when the data construction module executes construction of simulation data based on the target data segment, it is specifically used to:

[0080] Obtaining a third characteristic value based on the acquired vehicle data of the target vehicle model;

[0081] Extracting a preset number of data segments from the target data segments corresponding to each speed interval as intermediate data segments according to a preset extraction rule;

[0082] Loopingly executing the second process based on the intermediate data segment and the third characteristic value until a final data segment is determined;

[0083] Obtaining the simulation data based on the final data segment;

[0084] The second process includes:

[0085] determining a fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment;

[0086] Determining whether a second difference between the third eigenvalue and the fourth eigenvalue is less than a second difference threshold;

[0087] If it is determined that the second difference is less than the second difference threshold, taking the intermediate data segment as the final data segment;

[0088] If it is determined that the second difference is not less than the second difference threshold, determining whether the fourth eigenvalue is greater than the third eigenvalue;

[0089] If it is determined that the fourth eigenvalue is greater than the third eigenvalue, the fifth data segment is removed from the intermediate data segment, and the sixth data segment is used to update the intermediate data segment; wherein the fifth data segment is any one of the data segments in the intermediate data segment whose vehicle data is greater than the fourth eigenvalue, and the sixth data segment is any one of the data segments in the speed interval to which the fifth data segment belongs whose vehicle data is less than the fifth data segment;

[0090] If it is determined that the fourth eigenvalue is smaller than the third eigenvalue, the seventh data segment is removed from the intermediate data segment, and the eighth data segment is used to update the intermediate data segment; wherein the seventh data segment is any one of the data segments in the intermediate data segment whose vehicle data is smaller than the fourth eigenvalue, and the eighth data segment is any one of the data segments in the speed range to which the seventh data segment belongs whose vehicle data is greater than the seventh data segment.

[0091] In some possible embodiments, when the data construction module determines the fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment, it is specifically used to:

[0092] The fourth eigenvalue is obtained based on the vehicle data of the intermediate data segment and a preset algorithm.

[0093] In some possible embodiments, the splitting module performs splitting of the acquired vehicle data of the target vehicle model based on the preset driving speed to obtain at least one data segment, specifically for:

[0094] determining a timestamp corresponding to when the preset driving speed appears in the vehicle data;

[0095] The vehicle data obtained between two time stamps that are adjacent in time series is regarded as a data segment.

[0096] In the third aspect, another embodiment of the present application also provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any method provided in the embodiment of the first aspect of the present application.

[0097] In a fourth aspect, another embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute any method provided in the embodiment of the first aspect of the present application.

[0098] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0100] Figure 1 A schematic diagram of an application scenario of an energy consumption test method provided in an embodiment of the present application;

[0101] Figure 2 A schematic diagram of a flow chart of an energy consumption test method provided in an embodiment of the present application;

[0102] Figure 3 A schematic diagram of a flow chart of processing abnormal data in a data segment in an energy consumption testing method provided in an embodiment of the present application;

[0103] Figure 4 A schematic diagram of processing abnormal data in a data segment in an energy consumption test method provided in an embodiment of the present application;

[0104] Figure 5A schematic diagram of a process for determining a first characteristic value of an energy consumption test method provided in an embodiment of the present application;

[0105] Figure 6 A first flow chart of an energy consumption test method provided in an embodiment of the present application;

[0106] Figure 7 A schematic diagram of a process for constructing simulation data for an energy consumption test method provided in an embodiment of the present application;

[0107] Figure 8 A second flow chart of an energy consumption test method provided in an embodiment of the present application;

[0108] Fig. 9 A schematic diagram of the overall process of an energy consumption test method provided in an embodiment of the present application;

[0109] Fig.10 A schematic diagram of an energy consumption test method provided in an embodiment of the present application;

[0110] Fig.11 A schematic diagram of an electronic device for an energy consumption testing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0111] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0112] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0113] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0114] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0115] The embodiment of the present application provides an energy consumption test method, device, equipment and storage medium, which are used to solve the problem of inaccurate energy consumption test of vehicles in related technologies. The inventive concept of the present application can be summarized as follows: based on the preset driving speed, the vehicle data of the target vehicle model is split to obtain at least one data segment; at least one data segment is classified according to the preset calibration speed to obtain at least one speed interval; for each of the at least one speed interval, based on the vehicle data of the data segment contained in the speed interval, the first characteristic value of the speed interval is obtained; based on the first characteristic value, the target data segment is determined from the speed interval; based on the target data segment, simulation data is constructed; based on the simulation data and the pre-trained energy consumption test model, the energy consumption data of the target vehicle model is obtained. In the present application, by setting the data segments obtained by splitting the vehicle data to be classified, and determining the target data segment for constructing the simulation data based on each speed interval, the simulation data covers the vehicle data of different speed intervals, so that the simulation data is more similar to the actual situation of the vehicle driving in the city, and the simulation data is more representative and universal. The energy consumption data is obtained based on the preset energy consumption test model, and the accuracy of the obtained energy consumption data is guaranteed.

[0116] For ease of understanding, an energy consumption test method provided in an embodiment of the present application is described in detail below with reference to the accompanying drawings:

[0117] like Figure 1 As shown, it is an application scenario diagram of an energy consumption test method in an embodiment of the present application. The diagram includes: a server 10, a memory 20; wherein:

[0118] The server 10 splits the vehicle data of the target vehicle model obtained from the memory 20 based on the preset driving speed to obtain at least one data segment; classifies the at least one data segment according to the preset calibration speed to obtain at least one speed interval; for each of the at least one speed interval, based on the vehicle data of the data segment included in the speed interval, obtains a first eigenvalue of the speed interval; determines the target data segment from the speed interval based on the first eigenvalue; constructs simulation data based on the target data segment; and obtains energy consumption data of the target vehicle model based on the simulation data and a pre-trained energy consumption test model.

[0119] The description in this application only details a single server 10 and a storage 20, but those skilled in the art should understand that the server 10 and the storage 20 shown are intended to represent the operation of the server 10 and the storage 20 involved in the technical solution of this application. It does not imply any limitation on the number, type or location of the server 10 and the storage 20. It should be noted that if additional modules are added to the illustrated environment or individual modules are removed from it, the underlying concepts of the example embodiments of this application will not be changed. In addition, although for the convenience of explanation, Figure 1 A bidirectional arrow from the storage 20 to the server 10 is shown in the figure, but those skilled in the art can understand that the sending and receiving of the above data also needs to be implemented through the network.

[0120] It should be noted that the memory in the embodiment of the present application may be, for example, a cache system, a hard disk storage, a memory storage, etc. In addition, the energy consumption test method proposed in the present application is not only applicable to Figure 1 The application scenario shown is also applicable to any device that requires energy consumption testing.

[0121] like Figure 2 FIG. 1 is a flow chart of an energy consumption test method provided in an embodiment of the present application, wherein:

[0122] In step 201: the acquired vehicle data of the target vehicle type is split based on a preset driving speed to obtain at least one data segment.

[0123] In step 202: at least one data segment is classified according to a preset calibration speed to obtain at least one speed interval.

[0124] In step 203: for each of the at least one speed interval, based on the vehicle data of the data segment included in the speed interval, a first characteristic value of the speed interval is obtained; and based on the first characteristic value, a target data segment is determined from the speed interval.

[0125] In step 204: construct simulation data based on the target data segment.

[0126] In step 205: energy consumption data of the target vehicle model is obtained based on the simulation data and the pre-trained energy consumption test model.

[0127] It should be noted that in the present application, data segments are working conditions. In order to facilitate the understanding of the technical solutions proposed in the present application, working conditions are all referred to by data segments in the present application, so the constructed simulation data is a set of working conditions composed of multiple working conditions. In the present application, the data segments obtained by splitting the vehicle data are classified by setting, and the target data segments for constructing the simulation data are determined based on each speed range, so that the simulation data covers vehicle data in different speed ranges, making the simulation data more similar to the actual situation of the vehicle driving in the city, making the simulation data more representative and universal, and obtaining energy consumption data based on a preset energy consumption test model to ensure the accuracy of the obtained energy consumption data.

[0128] In order to further understand the energy consumption test method provided in the embodiment of the present application, Figure 2 The steps are described in detail:

[0129] In some possible embodiments, in order to ensure that the constructed simulation data can represent the actual situation of vehicles driving in the city, the vehicle data of the target model obtained in this application covers the vehicle data of vehicles driving on different road sections. In order to ensure the accuracy of the obtained simulation data, the vehicle data of the user group is collected through the vehicle computer. In other possible embodiments, when collecting the vehicle data of the user group, the remote communication terminal (Telematics Box, TBOX) can also upload the vehicle data in real time based on the vehicle network.

[0130] In some other possible embodiments, in order to ensure that the acquired vehicle data of the target vehicle model is representative in real time, when acquiring the vehicle data, the real-time data of the vehicle can be acquired through vehicle to X (V2X) wireless communication technology.

[0131] It should be noted that the two methods for collecting vehicle data given in this application are merely two embodiments, and are not intended to limit the method for collecting vehicle data in this application. During specific implementation, technicians can set the method for collecting vehicle data according to their needs.

[0132] In order to ensure accurate analysis of energy consumption at different vehicle speeds, after obtaining the vehicle data, it is necessary to split the vehicle data according to the preset driving speed to obtain at least one data segment. The specific implementation is as follows: determine the timestamp corresponding to when the preset driving speed appears in the vehicle data; and treat the vehicle data between two adjacent timestamps in time sequence as a data segment.

[0133] For example, for the acquired vehicle data 1, the preset driving speed is determined to be 0 km / h, and the timestamps of the preset driving speed in the vehicle data 1 are determined to be: 9:00, 11:00, 15:00, 17:00, then the vehicle data acquired between 9:00-11:00 is taken as data segment 1, the vehicle data acquired between 11:00-15:00 is taken as data segment 2, and the vehicle data acquired between 15:00-17:00 is taken as data segment 3. It is also determined that the average driving speed is 90 km / h starting at 0 km / h at 9:00 and driving until stopping at 11:00 (i.e., the vehicle speed drops to 0 km / h), the average driving speed is 60 km / h starting at 0 km / h from 11:00 and driving until stopping at 15:00, and the average driving speed is 40 km / h starting at 0 km / h from 15:00 and driving until stopping at 17:00. The time from starting at 0 km / h to stopping at 0 km / h is divided into one data segment, and the three data segments obtained are: data segment 1 (from 9:00 to 11:00, the average driving speed is 90 km / h), data segment 2 (from 11:00 to 15:00, the average driving speed is 60 km / h), and data segment 3 (from 15:00 to 17:00, the average driving speed is 40 km / h).

[0134] In order to ensure the similarity between the simulation data and the actual driving conditions of the vehicle, each data segment includes but is not limited to the following data: average vehicle speed, driving time, acceleration; since the processing method for each data in the data segment is the same, the average vehicle speed is used as an example for explanation below.

[0135] In some possible embodiments, the preset calibration speeds are determined to be 50 km / h and 80 km / h respectively. Then, three speed intervals can be obtained according to the preset calibration speeds, namely, a high-speed speed interval, a suburban speed interval, and an urban speed interval. For example, the data segments are respectively: data segment 1 (from A to B, the average driving speed is 90 km / h), data segment 2 (from B to C, the average driving speed is 60 km / h), and data segment 3 (from C to D, the average driving speed is 40 km / h). According to the average vehicle speeds corresponding to data segment 1, data segment 2, and data segment 3, it can be determined that data segment 1 belongs to the high-speed speed interval, data segment 2 belongs to the suburban speed interval, and data segment 3 belongs to the urban speed interval.

[0136] It should be noted that the preset calibrated speed is set by the technician based on the vehicle's driving scenario, that is, the specific value of the preset calibrated speed and the number of speed intervals are not limited in this application. During the specific implementation, the technician can set the value of the preset calibrated speed and the number of speed intervals according to the actual scenario.

[0137] In some possible embodiments, in order to ensure the accuracy of the obtained simulation data, after obtaining the data segment, it is necessary to process the abnormal data in the data segment, which can be specifically implemented as follows: Figure 3 Steps shown:

[0138] In step 301: for each data segment in the speed interval, determine whether the positioning data corresponding to the data segment is within the positioning data range; wherein the positioning data range is determined based on a preset calibration speed corresponding to the speed interval.

[0139] In step 302: if it is determined that the positioning data corresponding to the data segment is not within the positioning data range, the data segment is removed from the speed interval.

[0140] For example: Figure 4 As shown, for the urban speed interval, the urban speed interval includes: data segment 4, data segment 5, data segment 6, data segment 7, data segment 8, and the positioning data range corresponding to the urban speed interval is determined to be Figure 4 In the A range, the positioning data corresponding to data segments 4 to 8 are as follows Figure 4 As shown, it can be determined that the data segment 8 is not within the positioning data range, and the data segment 8 is removed from the urban speed range.

[0141] In the present application, data segments in the speed interval are screened based on the positioning data and the positioning data range, thereby avoiding the problem of inaccurate energy consumption data resulting from abnormal data segments participating in subsequent calculations.

[0142] In some possible embodiments, for each speed interval, a first characteristic value of the speed interval is obtained based on vehicle data of data segments contained in the speed interval. Specifically, it can be implemented as follows: the first characteristic value of the speed interval is obtained based on vehicle data of each data segment contained in the speed interval and a preset algorithm.

[0143] The preset algorithms may be the mean method, the majority method, the median method, and the weighted method. For ease of understanding, the following describes the methods for obtaining the first eigenvalue based on different preset algorithms:

[0144] When the default algorithm is the mean method, it can be implemented as follows Figure 5 The steps shown, where:

[0145] In step 501: a first mean value of vehicle data of each data segment included in a speed interval is determined.

[0146] In step 502: the first mean value is taken as the first characteristic value of the speed interval.

[0147] For example, for the urban speed interval, the urban speed interval includes: data segment 4, data segment 5, data segment 6, data segment 7, and data segment 8, wherein the average speed corresponding to data segment 4 is 50 km / h, the average speed corresponding to data segment 5 is 55 km / h, the average speed corresponding to data segment 6 is 57 km / h, the average speed corresponding to data segment 7 is 56 km / h, and the average speed corresponding to data segment 8 is 58 km / h. It can be determined that the first mean value of the vehicle data in the urban speed interval is 55.2 km / h, and the first eigenvalue of the urban speed interval is 55.2.

[0148] When the preset algorithm is the mode method, it can be implemented as follows: determining the mode of the vehicle data of each data segment included in the speed interval, and using the mode as the first eigenvalue of the speed interval.

[0149] For example, for the urban speed interval, the urban speed interval includes: data segment 4, data segment 5, data segment 6, data segment 7, and data segment 8, wherein the average speed corresponding to data segment 4 is 50 km / h, the average speed corresponding to data segment 5 is 50 km / h, the average speed corresponding to data segment 6 is 50 km / h, the average speed corresponding to data segment 7 is 52 km / h, and the average speed corresponding to data segment 8 is 49 km / h. It can be determined that the mode of vehicle data in the urban speed interval is 50 km / h, and the first eigenvalue of the urban speed interval is 50.

[0150] When the preset algorithm is the median, it can be implemented as follows: determining the median of the vehicle data of each data segment included in the speed interval, and using the median as the first characteristic value of the speed interval.

[0151] For example, for the urban speed interval, the urban speed interval includes: data segment 4, data segment 5, data segment 6, data segment 7, and data segment 8, wherein the average speed corresponding to data segment 4 is 50 km / h, the average speed corresponding to data segment 5 is 55 km / h, the average speed corresponding to data segment 6 is 57 km / h, the average speed corresponding to data segment 7 is 56 km / h, and the average speed corresponding to data segment 8 is 58 km / h. It can be determined that the median of the vehicle data in the urban speed interval is 56 km / h, and the first eigenvalue of the urban speed interval is 56.

[0152] When the preset algorithm is a weighted method, it can be implemented as follows: determining the mean, median, and mode of the vehicle data of each data segment included in the speed interval; and obtaining the first eigenvalue of the speed interval based on the weights corresponding to the mean, median, and mode, respectively.

[0153] For example, for the urban speed interval, the urban speed interval includes: data segment 4, data segment 5, data segment 6, data segment 7, and data segment 8, wherein the average speed corresponding to data segment 4 is 50 km / h, the average speed corresponding to data segment 5 is 50 km / h, the average speed corresponding to data segment 6 is 57 km / h, the average speed corresponding to data segment 7 is 56 km / h, and the average speed corresponding to data segment 8 is 58 km / h. It can be determined that the mean of the vehicle data in the urban speed interval is 54.2 km / h, the mode is 50 km / h, and the median is 56 km / h. The weight corresponding to the mean is determined to be 0.6, the weight corresponding to the mode is determined to be 0.3, and the weight corresponding to the median is determined to be 0.1, then the first eigenvalue of the urban speed interval is 53.26.

[0154] It should be noted that the four preset algorithms given above in this application are only four embodiments, and do not limit the specific implementation methods of the preset algorithms. During the specific implementation, technicians can determine the preset algorithms according to their needs.

[0155] In some possible embodiments, the target data segment is determined from the speed interval based on the first characteristic value, which can be specifically implemented as follows: a first number of data segments are selected from the speed interval according to a preset rule; the first process is executed cyclically for the first number of data segments until the target data segment is determined. The preset rule can be a random selection from the speed interval, or a sequential selection from the speed interval according to storage. In the specific implementation, the technician can set the preset rule according to the needs, and this application does not limit this. The first process can be implemented as follows Figure 6 The steps shown, where:

[0156] In step 601 : based on vehicle data of a first number of data segments, second characteristic values ​​of the first number of data segments are determined.

[0157] In some possible embodiments, when determining the second characteristic value, it can be implemented as follows: determining a second mean value of the vehicle data of the first number of data segments; and using the second mean value as the second characteristic value.

[0158] For example, if the first number is determined to be 5, the selected data segments are: vehicle data is data segment 4, data segment 5, data segment 6, data segment 7, and data segment 8, wherein the average speed corresponding to data segment 4 is 50 km / h, the average speed corresponding to data segment 5 is 55 km / h, the average speed corresponding to data segment 6 is 57 km / h, the average speed corresponding to data segment 7 is 56 km / h, and the average speed corresponding to data segment 8 is 58 km / h. It can be determined that the second mean value of the first number of data segments is 55.2 km / h, and the second eigenvalue is 55.2.

[0159] In step 602: determine whether the first difference between the second eigenvalue and the first eigenvalue is less than the first difference threshold; if so, proceed to step 603, otherwise proceed to step 604.

[0160] In step 603: a first number of data segments are used as target data segments.

[0161] In step 604: determine whether the second eigenvalue is greater than the first eigenvalue; if so, proceed to step 605, otherwise proceed to step 606.

[0162] In step 605: the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments; wherein the first data segment is any one of the data segments in the first number of data segments whose vehicle data is greater than the second characteristic value, and the second data segment is any one of the data segments in the speed range whose vehicle data is less than the first data segment.

[0163] In step 606: the third data segment is removed from the first number of data segments, and the fourth data segment is used to update the first number of data segments; wherein the third data segment is any one of the data segments in the first number of data segments whose vehicle data is less than the second characteristic value, and the fourth data segment is any one of the data segments in the speed range whose vehicle data is greater than the third data segment.

[0164] Example 1: The first eigenvalue is determined to be 55.2, and the selected data segments are vehicle data segments 9, 10, 11, 12, and 13, wherein the average speed corresponding to data segment 9 is 51 km / h, the average speed corresponding to data segment 10 is 55 km / h, the average speed corresponding to data segment 11 is 57 km / h, the average speed corresponding to data segment 12 is 56 km / h, and the average speed corresponding to data segment 13 is 58 km / h. The second eigenvalue is determined to be 55.4, and the first difference threshold is determined to be 1; the difference between the second eigenvalue and the first eigenvalue is 0.2, which is less than 1, and the first difference is determined to be less than the first difference threshold, then data segments 9, 10, 11, 12, and 13 are used as target data segments.

[0165] Example 2: Determine that the first eigenvalue is 55.2, and the data segments included in the speed interval are: data segment 9, data segment 10, data segment 11, data segment 12, data segment 13, data segment 14, data segment 15, data segment 16, data segment 17, and data segment 18, among which the average speed corresponding to data segment 9 is 52 km / h, the average speed corresponding to data segment 10 is 56 km / h, the average speed corresponding to data segment 11 is 60 km / h, the average speed corresponding to data segment 12 is 57 km / h, the average speed corresponding to data segment 13 is 58 km / h, the average speed corresponding to data segment 14 is 54 km / h, the average speed corresponding to data segment 15 is 62 km / h, the average speed corresponding to data segment 16 is 52 km / h, the average speed corresponding to data segment 17 is 63 km / h, and the average speed corresponding to data segment 18 is 64 km / h.

[0166] The first number is 5, and the 5 data segments selected are: data segment 9, data segment 10, data segment 11, data segment 12, and data segment 13; the second eigenvalue is determined to be 56.5, and the first difference threshold is determined to be 1; the difference between the second eigenvalue and the first eigenvalue is 1.3, which is greater than 1, and the second eigenvalue is determined to be greater than the first eigenvalue, then the data segments that can be used as the first data segment are determined to be: any one of data segment 11, data segment 12, and data segment 13, and when selecting, they can be selected in order from large to small according to the size of the vehicle data, and data segment 11 is selected as the first data segment, and the data segments that can be used as the second data segment are determined to be: data segment 14 and data segment 16; when selecting, they can be selected in order from small to large according to the vehicle data, and this time data segment 16 is selected as the second data segment.

[0167] Determine that the first number of data segments at this time are: data segment 9, data segment 10, data segment 16, data segment 12, data segment 13; determine that the second eigenvalue at this time is 55, and determine that the first difference threshold is 1; the difference between the second eigenvalue and the first eigenvalue is 0.2, which is less than 1, then data segment 9, data segment 10, data segment 16, data segment 12, and data segment 13 are used as target data segments.

[0168] Example 3: Determine that the first eigenvalue is 58, and the data segments included in the speed interval are: data segment 9, data segment 10, data segment 11, data segment 12, data segment 13, data segment 14, data segment 15, data segment 16, data segment 17, and data segment 18, among which the average speed corresponding to data segment 9 is 52 km / h, the average speed corresponding to data segment 10 is 56 km / h, the average speed corresponding to data segment 11 is 60 km / h, the average speed corresponding to data segment 12 is 57 km / h, the average speed corresponding to data segment 13 is 58 km / h, the average speed corresponding to data segment 14 is 54 km / h, the average speed corresponding to data segment 15 is 57 km / h, the average speed corresponding to data segment 16 is 52 km / h, the average speed corresponding to data segment 17 is 63 km / h, and the average speed corresponding to data segment 18 is 62 km / h.

[0169] The first number is 5, and the 5 data segments selected are: data segment 9, data segment 10, data segment 11, data segment 12, and data segment 13; the second eigenvalue is determined to be 56.5, and the first difference threshold is determined to be 1; the difference between the second eigenvalue and the first eigenvalue is 1.5, which is greater than 1, and the second eigenvalue is determined to be less than the first eigenvalue, then the data segments that can be used as the third data segment are determined to be: data segment 9, and when selecting, they can be selected in order from small to large according to the size of the vehicle data. At this time, only data segment 9 meets the requirements, so data segment 9 is selected as the third data segment, and the data segments that can be used as the fourth data segment are determined to be: data segment 14, data segment 15, data segment 17, and data segment 18; when selecting, they can be selected in order from large to small according to the vehicle data, so this time data segment 17 is selected as the fourth data segment.

[0170] Determine that the first number of data segments at this time are: data segment 17, data segment 10, data segment 11, data segment 12, data segment 13; determine that the second eigenvalue at this time is 58.5, and determine that the first difference threshold is 1; the difference between the second eigenvalue and the first eigenvalue is 0.5, which is less than 1, then data segment 17, data segment 10, data segment 11, data segment 12, and data segment 13 are used as target data segments.

[0171] In summary, through Figure 6 The steps in can determine the target data segment corresponding to each speed interval, and the vehicle data of the determined target data segment can accurately represent the average situation of the vehicle data in the speed interval.

[0172] In some possible embodiments, after determining the target data segments corresponding to each speed interval, when constructing simulation data based on the target data segments, it can be implemented as follows: extracting a corresponding preset number of data segments from the target data segments corresponding to each speed interval according to preset extraction rules to construct final data segments; and obtaining simulation data based on the final data segments corresponding to each speed interval.

[0173] In some possible embodiments, the preset extraction rules may be random extraction, or extraction according to the size of vehicle data corresponding to each target data segment. The present application does not limit the specific implementation method of the preset extraction rules. During the specific implementation, the technicians can determine the implementation method of the preset extraction rules according to their needs.

[0174] For example, the target data segments corresponding to the urban speed range are: data segment 1, data segment 2, data segment 3, data segment 4, data segment 5; the target data segments corresponding to the suburban speed range are: data segment 6, data segment 7, data segment 8, data segment 9, data segment 10; the target data segments corresponding to the high-speed speed range are: data segment 11, data segment 12, data segment 13, data segment 14, data segment 15; among which: the average speed corresponding to data segment 1 is 40 km / h, the average speed corresponding to data segment 2 is 45 km / h, the average speed corresponding to data segment 3 is 39 km / h, the average speed corresponding to data segment 4 is 44 km / h, The average speed corresponding to data segment 5 is 46 km / h; the average speed corresponding to data segment 6 is 51 km / h, the average speed corresponding to data segment 7 is 55 km / h, the average speed corresponding to data segment 8 is 57 km / h, the average speed corresponding to data segment 9 is 56 km / h, and the average speed corresponding to data segment 10 is 58 km / h; the average speed corresponding to data segment 11 is 62 km / h, the average speed corresponding to data segment 12 is 65 km / h, the average speed corresponding to data segment 13 is 70 km / h, the average speed corresponding to data segment 14 is 72 km / h, and the average speed corresponding to data segment 15 is 74 km / h.

[0175] The preset number of extractions corresponding to the urban speed range is 2, the preset number of extractions corresponding to the suburban speed range is 3, the preset number of extractions corresponding to the high-speed range is 2, and the preset extraction rule is random extraction. The final data segments are determined to be: data segment 2, data segment 3, data segment 8, data segment 9, data segment 10, data segment 11, and data segment 12; the constructed simulation data includes: vehicle data corresponding to data segment 2, data segment 3, data segment 8, data segment 9, data segment 10, data segment 11, and data segment 12, respectively.

[0176] It should be noted that the present application does not limit the specific numerical value of each speed interval corresponding to the preset extraction rule. The above is only an embodiment and does not limit the specific numerical value of the preset extraction rule.

[0177] In some other possible embodiments, in order to further ensure the similarity between the simulation data and the actual driving conditions of the vehicle, when constructing the simulation data based on the target data segment, it can also be implemented as follows: Figure 7 The steps shown, where:

[0178] In step 701: a third characteristic value is obtained based on the acquired vehicle data of the target vehicle model.

[0179] In some possible embodiments, the method for determining the third eigenvalue is the same as the method for determining the first eigenvalue, and this application will not elaborate on it here.

[0180] In step 702: according to a preset extraction rule, a preset number of data segments are extracted from the target data segments corresponding to each speed interval as intermediate data segments.

[0181] For example: the target data segments corresponding to the urban speed interval are: data segment 1, data segment 2, data segment 3, data segment 4, data segment 5; the target data segments corresponding to the suburban speed interval are: data segment 6, data segment 7, data segment 8, data segment 9, data segment 10; the target data segments corresponding to the high-speed speed interval are: data segment 11, data segment 12, data segment 13, data segment 14, data segment 15; the preset number of extractions corresponding to the urban speed interval is 2, the preset number of extractions corresponding to the suburban speed interval is 1, and the preset number of extractions corresponding to the high-speed interval is 2. The preset extraction rule is random extraction, then the intermediate data segments are determined to be: data segment 2, data segment 3, data segment 9, data segment 11, data segment 12.

[0182] In step 703: the second process is cyclically executed based on the intermediate data segment and the third eigenvalue until the final data segment is determined.

[0183] In some possible embodiments, the second process may be implemented as follows Figure 8 The steps shown, where:

[0184] In step 801 : a fourth characteristic value of the intermediate data segment is determined based on the vehicle data of the intermediate data segment.

[0185] In some possible embodiments, determining the fourth eigenvalue of the intermediate data segment based on the vehicle data of the intermediate data segment can be specifically implemented as follows: obtaining the fourth eigenvalue based on the vehicle data of the intermediate data segment and a preset algorithm. The method for obtaining the fourth eigenvalue based on the preset algorithm is the same as the method for obtaining the first eigenvalue, and will not be repeated here.

[0186] In step 802: determine whether the second difference between the third eigenvalue and the fourth eigenvalue is less than the second difference threshold; if so, proceed to step 803; otherwise, proceed to step 804;

[0187] In step 803: the intermediate data segment is used as the final data segment;

[0188] In step 804: determine whether the fourth eigenvalue is greater than the third eigenvalue, if so, proceed to step 805, otherwise proceed to step 806.

[0189] In step 805: the fifth data segment is removed from the intermediate data segments, and the sixth data segment is used to update the intermediate data segments; wherein the fifth data segment is any one of the data segments in the intermediate data segments whose vehicle data is greater than the fourth characteristic value, and the sixth data segment is any one of the data segments in the speed interval to which the fifth data segment belongs whose vehicle data is less than the fifth data segment;

[0190] In step 806: the seventh data segment is removed from the intermediate data segments, and the eighth data segment is used to update the intermediate data segments; wherein the seventh data segment is any one of the data segments in the intermediate data segments whose vehicle data is less than the fourth characteristic value, and the eighth data segment is any one of the data segments in the speed range to which the seventh data segment belongs whose vehicle data is greater than the vehicle data of the seventh data segment.

[0191] Example 4: The third eigenvalue is determined to be 55.6, and the intermediate data segments are: data segment 2, data segment 3, data segment 9, data segment 11, and data segment 12. The fourth eigenvalue is determined to be 53.4, and the second difference threshold is determined to be 1; the difference between the third eigenvalue and the fourth eigenvalue is 2.2, which is greater than 1, and the fourth eigenvalue is determined to be less than the third eigenvalue, then the data segments that can be used as the seventh data segment are determined to be: data segment 2 and data segment 3; when selecting the seventh data segment, the data can be selected in order from small to large according to the size of the vehicle data, and data segment 3 is selected as the seventh data segment.

[0192] It is determined that the speed interval to which the seventh data segment belongs is the urban speed interval, and the data segments included in the urban speed interval are: data segment 1, data segment 4, data segment 5; the data segments larger than the seventh data segment are: data segment 1, data segment 4, data segment 5, then when selecting the eighth data segment, you can select in order from large to small according to the size of the vehicle data, and select data segment 5 as the eighth data segment.

[0193] Determine that the intermediate data fragments at this time are: data fragment 2, data fragment 5, data fragment 9, data fragment 11, data fragment 12; determine that the fourth eigenvalue at this time is 54.8, and determine that the second difference threshold is 1, and the difference between the third eigenvalue and the fourth eigenvalue is 0.8, which is less than 1, then take data fragment 2, data fragment 5, data fragment 9, data fragment 11, and data fragment 12 as the final data fragments.

[0194] In step 704: simulation data is obtained based on the final data segment.

[0195] In some possible embodiments, in order to ensure that users can obtain the vehicle's energy consumption data in a timely manner, the vehicle data of the vehicle can be collected in real time through V2X technology and uploaded to the cloud. The cloud server performs the above-mentioned steps of calculating energy consumption based on the vehicle data of the vehicle, and then obtains the energy consumption data of the vehicle. The time when the vehicle energy consumption data is obtained is associated with the energy consumption data to obtain real-time energy consumption data. The real-time energy consumption data is sent to the vehicle based on the vehicle network, and the vehicle can display the real-time energy consumption data on the vehicle screen; in other possible embodiments, after obtaining the real-time energy consumption data, the vehicle can send the real-time energy consumption data to the smart terminal bound to the vehicle, and display the real-time energy consumption data of the vehicle in the smart terminal. Through this method, users can understand the energy consumption of the vehicle in real time, ensuring the user experience.

[0196] In some possible embodiments, the training process of the energy consumption test model used in the present application is the same as the training process of the neural network model in the related art, and will not be repeated here.

[0197] In order to facilitate further understanding of an energy consumption test method provided in an embodiment of the present application, the overall process of the energy consumption test method provided in the present application is described in detail below. Fig. 9 As shown, where:

[0198] In step 901: the acquired vehicle data of the target vehicle model is split based on a preset driving speed to obtain at least one data segment.

[0199] In step 902: at least one data segment is classified according to a preset calibration speed to obtain at least one speed interval.

[0200] In step 903: for each data segment in the speed interval, determine whether the positioning data corresponding to the data segment is within the positioning data range, if not, proceed to step 904, otherwise proceed to step 905;

[0201] In step 904: the data segment is removed from the speed interval.

[0202] In step 905 : for each of the at least one speed interval, based on the vehicle data of the data segment included in the speed interval, a first characteristic value of the speed interval is obtained.

[0203] In step 906: a first number of data segments are selected from the speed interval according to a preset rule.

[0204] In step 907 : based on the vehicle data of the first number of data segments, second characteristic values ​​of the first number of data segments are determined.

[0205] In step 908 : determine whether the first difference between the second eigenvalue and the first eigenvalue is less than the first difference threshold; if so, proceed to step 909 , otherwise proceed to step 910 .

[0206] In step 909: the first number of data segments are used as target data segments.

[0207] In step 910: determine whether the second eigenvalue is greater than the first eigenvalue; if so, proceed to step 911, otherwise proceed to step 912.

[0208] In step 911: the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments.

[0209] In step 912: the third data segment is removed from the first number of data segments, and the first number of data segments is updated with the fourth data segment.

[0210] In step 913: a third characteristic value is obtained based on the acquired vehicle data of the target vehicle model.

[0211] In step 914: according to a preset extraction rule, a preset number of data segments are extracted from the target data segments corresponding to each speed interval as intermediate data segments.

[0212] In step 915 : a fourth characteristic value of the intermediate data segment is determined based on the vehicle data of the intermediate data segment.

[0213] In step 916: determine whether the second difference between the third eigenvalue and the fourth eigenvalue is less than the second difference threshold, if so, proceed to step 918, otherwise proceed to step 917.

[0214] In step 917: the intermediate data segment is used as the final data segment

[0215] In step 918: determine whether the fourth eigenvalue is greater than the third eigenvalue, if so, proceed to step 919, otherwise proceed to step 920.

[0216] In step 919: the fifth data segment is removed from the intermediate data segment, and the sixth data segment is used to update the intermediate data segment.

[0217] In step 920: the seventh data segment is removed from the intermediate data segments, and the eighth data segment is used to update the intermediate data segments.

[0218] In step 921: obtain simulation data based on the final data segment.

[0219] In step 922: energy consumption data of the target vehicle model is obtained based on the simulation data and the pre-trained energy consumption test model.

[0220] Based on the same inventive concept, the present application also provides an energy consumption test device 100, such as Fig.10 As shown, the device comprises:

[0221] The splitting module 10001 is used to split the acquired vehicle data of the target vehicle type based on a preset driving speed to obtain at least one data segment;

[0222] A classification module 10002, configured to classify the at least one data segment according to the preset calibration speed to obtain at least one speed interval;

[0223] The target data segment determination module 10003 is used to obtain, for each of the at least one speed interval, a first characteristic value of the speed interval based on the vehicle data of the data segment included in the speed interval; and determine a target data segment from the speed interval based on the first characteristic value;

[0224] A data construction module 10004 is used to construct simulation data based on the target data segment;

[0225] The testing module 10005 is used to obtain the energy consumption data of the target vehicle model based on the simulation data and a pre-trained energy consumption test model.

[0226] In some possible embodiments, when the target data segment determination module 10003 executes the vehicle data based on the data segments included in the speed interval to obtain the first characteristic value of the speed interval, it is specifically used to:

[0227] A first characteristic value of the speed interval is obtained based on the vehicle data of each data segment included in the speed interval and a preset algorithm.

[0228] In some possible embodiments, before the target data segment determination module 10003 executes the vehicle data based on the data segments included in the speed interval to obtain the first characteristic value of the speed interval, it is also used to:

[0229] For each data segment in the speed interval, determining whether the positioning data corresponding to the data segment is within a positioning data range; wherein the positioning data range is determined based on a preset calibration speed corresponding to the speed interval;

[0230] If it is determined that the positioning data corresponding to the data segment is not within the positioning data range, the data segment is removed from the speed interval.

[0231] In some possible embodiments, when the target data segment determination module 10003 determines the target data segment from the speed interval based on the first characteristic value, it is specifically used to:

[0232] Selecting a first number of data segments from the speed interval according to a preset rule;

[0233] The first process is cyclically executed for the first number of data segments until a target data segment is determined; wherein the first process includes:

[0234] determining second characteristic values ​​of the first number of data segments based on the vehicle data of the first number of data segments;

[0235] determining whether a first difference between the second eigenvalue and the first eigenvalue is less than a first difference threshold;

[0236] If it is determined that the first difference is less than the first difference threshold, taking the first number of data segments as target data segments;

[0237] If it is determined that the first difference is not less than the first difference threshold, determining whether the second eigenvalue is greater than the first eigenvalue;

[0238] If it is determined that the second characteristic value is greater than the first characteristic value, the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments; wherein the first data segment is any one of the data segments in the first number of data segments whose vehicle data is greater than the second characteristic value, and the second data segment is any one of the data segments in the speed range whose vehicle data is less than the first data segment;

[0239] If it is determined that the second eigenvalue is smaller than the first eigenvalue, the third data segment is eliminated from the first number of data segments, and the fourth data segment is used to update the first number of data segments; wherein the third data segment is any one of the data segments in the first number of data segments whose vehicle data is smaller than the second eigenvalue, and the fourth data segment is any one of the data segments in the speed range whose vehicle data is larger than the third data segment.

[0240] In some possible embodiments, when the target data segment determination module 10003 determines the second characteristic values ​​of the first number of data segments based on the vehicle data of the first number of data segments, it is specifically used to:

[0241] The second characteristic value is obtained based on the vehicle data of the first number of data segments and a preset algorithm.

[0242] In some possible embodiments, when the data construction module 10004 executes the construction of simulation data based on the target data segment, it is specifically used to:

[0243] According to a preset extraction rule, a preset number of data segments are extracted from the target data segments corresponding to each speed interval to construct a final data segment;

[0244] The simulation data is obtained based on the final data segment corresponding to each speed interval.

[0245] In some possible embodiments, when the data construction module 10004 executes the construction of simulation data based on the target data segment, it is specifically used to:

[0246] Obtaining a third characteristic value based on the acquired vehicle data of the target vehicle model;

[0247] Extracting a preset number of data segments from the target data segments corresponding to each speed interval as intermediate data segments according to a preset extraction rule;

[0248] Loopingly executing the second process based on the intermediate data segment and the third characteristic value until a final data segment is determined;

[0249] Obtaining the simulation data based on the final data segment;

[0250] The second process includes:

[0251] determining a fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment;

[0252] Determining whether a second difference between the third eigenvalue and the fourth eigenvalue is less than a second difference threshold;

[0253] If it is determined that the second difference is less than the second difference threshold, taking the intermediate data segment as the final data segment;

[0254] If it is determined that the second difference is not less than the second difference threshold, determining whether the fourth eigenvalue is greater than the third eigenvalue;

[0255] If it is determined that the fourth eigenvalue is greater than the third eigenvalue, the fifth data segment is removed from the intermediate data segment, and the sixth data segment is used to update the intermediate data segment; wherein the fifth data segment is any one of the data segments in the intermediate data segment whose vehicle data is greater than the fourth eigenvalue, and the sixth data segment is any one of the data segments in the speed interval to which the fifth data segment belongs whose vehicle data is less than the fifth data segment;

[0256] If it is determined that the fourth eigenvalue is smaller than the third eigenvalue, the seventh data segment is removed from the intermediate data segment, and the eighth data segment is used to update the intermediate data segment; wherein the seventh data segment is any one of the data segments in the intermediate data segment whose vehicle data is smaller than the fourth eigenvalue, and the eighth data segment is any one of the data segments in the speed range to which the seventh data segment belongs whose vehicle data is greater than the seventh data segment.

[0257] In some possible embodiments, when the data construction module 10004 determines the fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment, it is specifically used to:

[0258] The fourth eigenvalue is obtained based on the vehicle data of the intermediate data segment and a preset algorithm.

[0259] In some possible embodiments, the splitting module 10001 performs splitting of the acquired vehicle data of the target vehicle model based on the preset driving speed to obtain at least one data segment, which is specifically used to:

[0260] determining a timestamp corresponding to when the preset driving speed appears in the vehicle data;

[0261] The vehicle data obtained between two time stamps that are adjacent in time series is regarded as a data segment.

[0262] Corresponding to the above embodiments, the present application also provides an electronic device. Fig.11A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, the electronic device 1100 may include: a processor 1101, a memory 1102 and a communication unit 1103. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of the present invention, it can be a bus structure or a star structure, and can also include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0263] The communication unit 1103 is used to establish a communication channel so that the electronic device can communicate with other devices, receive user data sent by other devices or send user data to other devices.

[0264] The processor 1101 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 1102, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 1101 can include only a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0265] The memory 1102 is used to store the execution instructions of the processor 1101. The memory 1102 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, magnetic disk or optical disk.

[0266] When the execution instructions in the memory 1102 are executed by the processor 1101, the electronic device 1100 can execute Figure 7 Some or all of the steps in the illustrated embodiments.

[0267] In a specific implementation, the present invention further provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment of the calling method provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0268] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0269] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

Claims

1. An energy consumption test method, characterized in that: The method comprises: Splitting the acquired vehicle data of the target vehicle model based on a preset driving speed to obtain at least one data segment; Classifying the at least one data segment according to the preset calibration speed to obtain at least one speed interval; For each of the at least one speed interval, based on the vehicle data of the data segments contained in the speed interval, a first characteristic value of the speed interval is obtained; based on the first characteristic value, a target data segment is determined from the speed interval; wherein, the determination of the target data segment from the speed interval based on the first characteristic value includes: selecting a first number of data segments from the speed interval according to a preset rule; cyclically executing a first process for the first number of data segments until the target data segment is determined; wherein the first process includes: based on the vehicle data of the first number of data segments, determining a second characteristic value of the first number of data segments; determining whether a first difference between the second characteristic value and the first characteristic value is less than a first difference threshold; if it is determined that the first difference is less than the first difference threshold, taking the first number of data segments as the target data segment; if it is determined that the first difference is not less than the first difference threshold, determining whether the second characteristic value is greater than the first characteristic value; if it is determined that the second characteristic value is greater than the first characteristic value, the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments; wherein the first data segment is any one of the data segments in the first number of data segments whose vehicle data is greater than the second characteristic value, and the second data segment is any one of the data segments of the vehicle data in the speed interval whose vehicle data is less than the first data segment; if it is determined that the second characteristic value is less than the first characteristic value, the third data segment is removed from the first number of data segments, and the fourth data segment is any one of the data segments of the vehicle data in the speed interval whose vehicle data is greater than the third data segment; constructing simulation data based on the target data segment; The energy consumption data of the target vehicle model is obtained based on the simulation data and a pre-trained energy consumption test model.

2. The method according to claim 1, characterized in that The obtaining of the first characteristic value of the speed interval based on the vehicle data of the data segments included in the speed interval includes: A first characteristic value of the speed interval is obtained based on the vehicle data of each data segment included in the speed interval and a preset algorithm.

3. The method according to claim 1, characterized in that Before obtaining the first characteristic value of the speed interval based on the vehicle data of the data segments included in the speed interval, the method further includes: For each data segment in the speed interval, determining whether the positioning data corresponding to the data segment is within a positioning data range; wherein the positioning data range is determined based on a preset calibration speed corresponding to the speed interval; If it is determined that the positioning data corresponding to the data segment is not within the positioning data range, the data segment is removed from the speed interval.

4. The method according to claim 1, characterized in that: The determining, based on the vehicle data of the first number of data segments, second characteristic values ​​of the first number of data segments comprises: The second characteristic value is obtained based on the vehicle data of the first number of data segments and a preset algorithm.

5. The method according to claim 1, characterized in that: The step of constructing simulation data based on the target data segment comprises: According to a preset extraction rule, a preset number of data segments are extracted from the target data segments corresponding to each speed interval to construct a final data segment; The simulation data is obtained based on the final data segment corresponding to each speed interval.

6. The method according to claim 1, characterized in that The step of constructing simulation data based on the target data segment comprises: Obtaining a third characteristic value based on the acquired vehicle data of the target vehicle model; Extracting a preset number of data segments from the target data segments corresponding to each speed interval as intermediate data segments according to a preset extraction rule; Loopingly executing the second process based on the intermediate data segment and the third characteristic value until a final data segment is determined; Obtaining the simulation data based on the final data segment; The second process includes: determining a fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment; Determining whether a second difference between the third eigenvalue and the fourth eigenvalue is less than a second difference threshold; If it is determined that the second difference is less than the second difference threshold, taking the intermediate data segment as the final data segment; If it is determined that the second difference is not less than the second difference threshold, determining whether the fourth eigenvalue is greater than the third eigenvalue; If it is determined that the fourth eigenvalue is greater than the third eigenvalue, the fifth data segment is removed from the intermediate data segment, and the sixth data segment is used to update the intermediate data segment; wherein the fifth data segment is any one of the data segments in the intermediate data segment whose vehicle data is greater than the fourth eigenvalue, and the sixth data segment is any one of the data segments in the speed interval to which the fifth data segment belongs whose vehicle data is less than the fifth data segment; If it is determined that the fourth eigenvalue is smaller than the third eigenvalue, the seventh data segment is removed from the intermediate data segment, and the eighth data segment is used to update the intermediate data segment; wherein the seventh data segment is any one of the data segments in the intermediate data segment whose vehicle data is smaller than the fourth eigenvalue, and the eighth data segment is any one of the data segments in the speed range to which the seventh data segment belongs whose vehicle data is greater than the seventh data segment.

7. The method according to claim 6, characterized in that The determining of the fourth characteristic value of the intermediate data segment based on the vehicle data of the intermediate data segment comprises: The fourth eigenvalue is obtained based on the vehicle data of the intermediate data segment and a preset algorithm.

8. The method according to claim 1, characterized in that The step of splitting the acquired vehicle data of the target vehicle model based on the preset driving speed to obtain at least one data segment includes: determining a timestamp corresponding to when the preset driving speed appears in the vehicle data; The vehicle data obtained between two time stamps that are adjacent in time series is regarded as a data segment.

9. An energy consumption test device, characterized in that: The device comprises: A splitting module, used to split the acquired vehicle data of the target vehicle model based on a preset driving speed to obtain at least one data segment; A classification module, configured to classify the at least one data segment according to the preset calibration speed to obtain at least one speed interval; A target data segment determination module is used to obtain, for each of the at least one speed interval, a first characteristic value of the speed interval based on the vehicle data of the data segments contained in the speed interval; and determine a target data segment from the speed interval based on the first characteristic value; wherein the target data segment determination module is specifically used to: select a first number of data segments from the speed interval according to a preset rule; cyclically execute a first process for the first number of data segments until a target data segment is determined; wherein the first process includes: determining a second characteristic value of the first number of data segments based on the vehicle data of the first number of data segments; determining whether a first difference between the second characteristic value and the first characteristic value is less than a first difference threshold; if it is determined that the first difference is less than the first difference threshold, using the first number of data segments as the target data segment; if it is determined that the first difference is not less than the first difference threshold, determining whether the second characteristic value is greater than or equal to the first difference threshold. if it is determined that the second eigenvalue is greater than the first eigenvalue, the first data segment is removed from the first number of data segments, and the second data segment is used to update the first number of data segments; wherein the first data segment is any one of the data segments in the first number of data segments whose vehicle data is greater than the second eigenvalue, and the second data segment is any one of the data segments in the speed interval whose vehicle data is less than the first data segment; if it is determined that the second eigenvalue is less than the first eigenvalue, the third data segment is removed from the first number of data segments, and the fourth data segment is any one of the data segments in the speed interval whose vehicle data is greater than the third data segment; A data construction module, used for constructing simulation data based on the target data segment; The test module is used to obtain the energy consumption data of the target vehicle model based on the simulation data and a pre-trained energy consumption test model.

10. An electronic device, characterized in that: The device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 8.

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