CN112836395A - Vehicle driving data simulation method and device, electronic equipment and storage medium - Google Patents
Vehicle driving data simulation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a vehicle driving data simulation method, a device, an electronic device and a storage medium, wherein the method comprises the following steps: collecting driving data of a vehicle in different physical environments; and inputting the running data into a set machine learning model to perform vehicle running data simulation through the set machine learning model to obtain a set number of vehicle running simulation data, wherein the set number is larger than the data amount of the running data. By the technical scheme of the embodiment of the disclosure, high simulation of vehicle driving data is realized, and the problems of high distortion degree, insufficient data volume and the like of service test data are solved.
Description
Technical Field
The present disclosure relates to the field of vehicle technologies, and in particular, to a vehicle driving data simulation method and apparatus, an electronic device, and a storage medium.
Background
With the improvement of living standard of people, automobiles are popularized to thousands of households, and the demand of the automobiles is increased day by day. Therefore, the stability of various properties of automobiles is increasingly important. Before or even after the automobile leaves a factory, various performances of the automobile need to be effectively tested and monitored.
Due to the high cost and limited data volume for acquiring the driving data of the automobile in the real physical environment, various driving data are required to be simulated manually.
The following problems exist in the manual simulation of the driving data: the human cost is higher, the efficiency is lower, the data volume is still insufficient and the distortion factor of the data is higher. The above problems result in the inability to meet performance tests before various services come online and the compatibility tests of services with real driving data.
Disclosure of Invention
In order to solve the technical problems described above or at least partially solve the technical problems, the present disclosure provides a vehicle driving data simulation method, apparatus, electronic device, and storage medium.
In a first aspect, an embodiment of the present disclosure provides a vehicle driving data simulation method, including:
collecting driving data of a vehicle in different physical environments;
and inputting the running data into a set machine learning model to perform vehicle running data simulation through the set machine learning model to obtain a set number of vehicle running simulation data, wherein the set number is larger than the data amount of the running data.
In a second aspect, the disclosed embodiments also provide a vehicle driving data simulation apparatus, which includes:
the acquisition module is used for acquiring running data of the vehicle in different physical environments;
the acquisition module is used for inputting the driving data into a set machine learning model so as to carry out vehicle driving data simulation through the set machine learning model and obtain a set number of vehicle driving simulation data, wherein the set number is larger than the data volume of the driving data.
In a third aspect, an embodiment of the present disclosure further provides an apparatus, where the apparatus includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle travel data simulation method according to any one of the embodiments of the present disclosure.
In a fourth aspect, the disclosed embodiments also provide a storage medium containing computer-executable instructions for performing the vehicle driving data simulation method according to any one of the disclosed embodiments when executed by a computer processor.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
by means of the machine learning model, more vehicle running simulation data are generated based on the collected running data of the vehicle in the real environment, high simulation and batch generation of the vehicle running simulation data are achieved, data simulation efficiency is improved, labor cost is reduced, and the problems that service test data are high in distortion degree, insufficient in data quantity and the like are solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a vehicle driving data simulation method according to a first embodiment of the disclosure;
fig. 2 is a schematic flow chart of a vehicle driving data simulation method according to a second embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a vehicle driving data simulation method according to a third embodiment of the present disclosure;
fig. 4 is a schematic diagram of an analysis framework of a vehicle driving data simulation based on a machine learning technique according to a third embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a vehicle driving data simulation apparatus according to a fourth embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to a fifth embodiment of the disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
Example one
Fig. 1 is a schematic flow chart of a vehicle driving data simulation method according to a first embodiment of the disclosure. The method may be performed by a vehicle driving data simulation apparatus, which may be implemented in the form of software and/or hardware.
As shown in fig. 1, the vehicle driving data simulation method provided by the present embodiment includes the following steps:
and step 110, collecting driving data of the vehicle in different physical environments.
The different physical environments may be, for example, a low-temperature environment (e.g., northeastern areas in winter), a high-temperature environment (e.g., southeast areas in summer), a weak-network environment (e.g., a tunnel, a remote area), a non-network environment, a plateau, a basin, a mountain or a river, or the like. The method comprises the steps of driving a vehicle to run in a real physical environment, and collecting various running data generated in the real running process of the vehicle.
Illustratively, the travel data includes at least one of: battery temperature, battery amperage, four-wheel tire pressure, motor speed, compressor speed, tire lateral acceleration, battery charge consumption, and fuel consumption.
Further, the acquiring of the driving data of the vehicle in different physical environments includes:
the driving data of the vehicle under different physical environments is acquired through the vehicle-mounted sensor.
The onboard sensor includes at least one of:
image sensors (e.g., tachographs), sound sensors, radar sensors (e.g., millimeter wave radar), and Beidou positioning sensors. The Beidou positioning sensor is a self-developed global satellite navigation system by China, consists of a null section, a ground section and a user section, can provide high-precision, high-reliability positioning, navigation and time service for various users all day long in the global range, has short message communication capacity, and initially has regional navigation, positioning and time service capacity.
In addition to the above listed vehicle-mounted sensors, other data acquisition devices may be included, which are not limited to sensors, but may also be specific measurement circuits, etc., as long as they can detect various performance parameters and index values (e.g., network speed) of the vehicle during driving.
And 120, inputting the running data into a set machine learning model to perform vehicle running data simulation through the set machine learning model to obtain a set number of vehicle running simulation data, wherein the set number is larger than the data amount of the running data.
The purpose of collecting the running data of the vehicle in different physical environments is as follows: on the one hand, the training sample is used for setting the machine learning model, and on the other hand, the initial reference standard is used for setting the machine learning model to perform data simulation, or the initial reference standard is understood to be trigger data for setting the machine learning model to perform data simulation. For example, the inputs to set the machine learning model are: if the vehicle runs under different physical environments, the simulation data of the vehicle running output by the set machine learning model is also the signal data of the battery when the vehicle runs under different physical environments. The data volume output by the set machine learning model is far larger than the collected real data. Therefore, extreme values, median values and the like of the battery signal data of the vehicle under different physical environments can be obtained by setting a machine learning model to simulate the vehicle running data.
The vehicle running simulation data output by the machine learning model is specifically set to include: signal name (e.g., cell voltage), signal value (e.g., 5v), time relationship between signals (e.g., cell voltage 5v at 10 o 'clock by 1 second, cell voltage 5.1v at 10 o' clock by 2 seconds).
It can be understood that, when the real data is collected, the collection is performed according to a certain collection frequency, the collection frequency is limited by hardware and software technologies, the collection frequency cannot be infinitely high, and in consideration of cost, the vehicle cannot be driven to run all the time in a real environment for a long time, so that the data volume of the collected real data is limited, and therefore, the simulation operation of the vehicle running data needs to be further performed by setting a machine learning model, so that more vehicle running simulation data are obtained.
According to the technical scheme, more vehicle running simulation data are generated based on the collected running data of the vehicle in the real environment by means of the machine learning model, high simulation and batch simulation of the vehicle running simulation data are achieved, data simulation efficiency is improved, labor cost is reduced, and the problems that service test data are high in distortion degree and insufficient in data quantity are solved.
Example two
Fig. 2 is a schematic flow chart of a vehicle driving data simulation method according to a second embodiment of the present disclosure. On the basis of the above-described embodiment, the present embodiment adds an operation of storing the running data and an operation of transmitting the running data and the vehicle running simulation data to a target service. Accumulation of real running data can be achieved by storing the running data, and the purpose of enabling a target service to carry out a service performance test based on the running data and the vehicle running simulation data is achieved by sending the running data and the vehicle running simulation data to the target service. The same or similar contents as those in the above embodiments are not repeated in this embodiment, and for the related explanation, reference may be made to the above embodiments.
As shown in fig. 2, the vehicle driving data simulation method includes the steps of:
and step 210, collecting driving data of the vehicle in different physical environments.
And step 220b, storing the driving data to a preset distributed file system.
Illustratively, the preset Distributed File System may be an HDFS (Hadoop Distributed File System). The HDFS is a distributed file system suitable for running on general hardware (comfort hardware), is a system with high fault tolerance, can provide data access with high throughput, is suitable for application on a large-scale data set, and can achieve the purpose of reading file system data in a streaming mode. In the technical scheme of this embodiment, the operation of collecting the driving data of the vehicle in different physical environments is not performed only once, and may be performed once at intervals according to the requirements of various items, and the real driving data of the vehicle in different physical environments is sample data with the highest reliability, so that the real driving data of the vehicle in different physical environments needs to be accumulated and reliably stored.
And step 230, sending the running data and the vehicle running simulation data to a target service so that the target service performs a service performance test based on the running data and the vehicle running simulation data, and outputting an analysis report based on a test log.
Illustratively, the outputting an analysis report based on the test log includes:
identifying preset key fields in the test log;
determining the error meaning corresponding to the preset key field;
generating an analysis report according to the error meaning;
and outputting the analysis report to a processing platform matched with the wrong meaning.
The preset key fields are, for example: the interface returns exception status codes (including without limitation 404, 500, 502, etc.), exception captures of the code (including without limitation NullPointerException, ArrayIndexOutOfBoundsException, ClassCastException, etc.), and custom code exception keys (including without limitation Fail, Erro, TimeOut, etc.). The corresponding error meaning of the abnormal status code 404 is: the requested web page does not exist; the corresponding error meaning of the abnormal status code 500 is: the service is in internal error, and the service cannot complete the request when encountering the error; the corresponding error meaning of the abnormal status code 502 is: the wrong gateway, the service acting as a gateway or proxy, receives an invalid response from upstream.
The target service performs a service performance test based on the driving data and the vehicle driving simulation data, and outputting an analysis report based on a test log may specifically be understood as: for example, it is desirable for an alert service to implement the following functions: if the battery temperature signal EBS _ T _ BATT > is 70 and the continuous reporting time exceeds 5s, namely the battery temperature signal EBS _ T _ BATT reported by the vehicle is greater than or equal to 70 ℃ and the battery temperature signal is not less than 70 after the duration time exceeds 5s, an alarm is triggered, and the alarm service can send the reported battery temperature signal, the signal value and the signal occurrence time to the battery research and development platform, so that the battery research and development platform can accurately position and monitor the high-temperature battery on the line, and more serious problems can be prevented. In the process of testing the alarm service, the battery temperature signal EBS _ T _ BATT > is input to 70, and the test data with the time exceeding 5s is continuously reported, and the alarm service performs the alarm operation as described above, so that through the test, it is determined that the performance of the alarm service is superior, and the alarm service can be released to the online for formal use. Whether the alarm service performs the alarm operation as described above may be determined by the test log output by the alarm service.
And 240, receiving an analysis report returned by the service, and distributing an application scene for the running data and the vehicle running simulation data according to the analysis report.
Specifically, if the analysis report shows that each performance of the service is superior, the quality of the test data of the service is good, and therefore, a fixed application scenario may be assigned to the driving data and the vehicle driving simulation data, and then, if the test data in the application scenario is needed, the driving data and the vehicle driving simulation data may be preferably used. For example, when the alarm service is tested, it is shown that the alarm service test is passed, and the alarm service can actually and effectively alarm the high-temperature battery after being on-line, which indicates that the quality of the test data is better, and the function of the alarm service can be effectively tested, the test data adopted by the alarm service is associated with the application scene of "high-temperature battery alarm", indicating that the test data is more suitable for testing the application of the high-temperature battery alarm. In the proceeding process of the subsequent project, if the test data of the high-temperature battery alarm is needed again, the test data allocated to the application scene can be directly called.
Further, if a target service is tested through the running data and the vehicle running simulation data and the service passing the test is determined to be better represented on line (for example, an alarm service can accurately alarm a high-temperature battery), the running data and the vehicle running simulation data are stored as new samples, so that the machine learning model is iteratively trained based on the new samples.
If the target service is tested through the running data and the vehicle running simulation data and the service passing the test is determined to be poor in online performance (for example, an alarm service cannot effectively alarm a high-temperature battery, for example, the number of times of alarm is 5, and the alarm service only alarms 2 times), a new sample is added on the basis of the running data and the vehicle running simulation data based on a set strategy to serve as a corrected training data set. Specifically, the on-line performance of the target service does not meet the set standard due to what reason the manual intervention analysis is needed, if the on-line performance is caused by the test data, the service is guided to expose errors in the test process by manually simulating new test data (namely new samples), and if the on-line performance is the logic problem of the service, the logic of the service itself needs to be adjusted.
Illustratively, the method further comprises:
and correcting the training data set of the machine learning model according to the analysis report and the on-line performance of the target service, and performing iterative training on the machine learning model based on the corrected training data set so as to enable the performance of the service obtained by testing the vehicle driving simulation data output by the machine learning model after iterative training to reach a target value.
Further, the modifying the training data set of the machine learning model according to the analysis report and the online performance of the target service includes:
if the analysis report shows that the test is passed and the on-line performance of the target service reaches a set standard (for example, an alarm service can accurately alarm a high-temperature battery, for example, the number of times of alarm is 5, and the alarm service alarms 5 times at a proper time point), taking the driving data and the vehicle driving simulation data as a corrected training data set;
if the analysis report shows that the test is passed and the on-line performance of the target service does not meet the set standard (for example, the warning service cannot accurately warn the high-temperature battery, for example, the number of times of warning is 5, the warning service only warns for 2 times, and the set standard warns for 4 times), adding a new sample as a corrected training data set on the basis of the driving data and the vehicle driving simulation data based on the set strategy.
Optionally, the corrected training data set is stored in a preset distributed file system HDFS, so as to be read when iterative training needs to be performed on the set machine learning model.
According to the technical scheme of the embodiment of the disclosure, on the basis of the embodiment, the operation of storing the running data and the operation of sending the running data and the vehicle running simulation data to a target service are added. Accumulation of the running data can be realized by storing the running data, and the aim of enabling the target service to carry out service performance test based on the running data and the vehicle running simulation data is realized by sending the running data and the vehicle running simulation data to the target service. And further correcting the training sample set according to the test result and the online performance of the target service so as to improve the quality of the training sample set, improve the iterative training effect of the machine learning model and enable the machine learning model to have a better data simulation function. The vehicle running simulation data are generated based on the collected running data of the vehicle in the real environment by means of the machine learning model, high simulation and batch simulation of the vehicle running simulation data are achieved, data simulation efficiency is improved, labor cost is reduced, sufficient high-quality data are provided for performance test of target service, the problem of data distortion of the test required by the performance test before the target service is on line is solved, and meanwhile, the problem of rapid vehicle fault location by the service is facilitated by providing massive high-simulation running data for the service.
EXAMPLE III
Fig. 3 is a schematic flow chart of a vehicle driving data simulation method according to a third embodiment of the present disclosure. On the basis of the embodiment, before the running data and the vehicle running simulation data are sent to the target service, the operation of processing the data format of the running data and the vehicle running simulation data is added, so that the processed data format accords with the data reading rule of the target service, the target service can directly read the running data and the vehicle running simulation data, and then the performance test is carried out, the format processing of the running data and the vehicle running simulation data is not needed, and the efficiency of testing the service performance is improved. The same or similar contents as those in the above embodiments are not repeated in this embodiment, and for the related explanation, reference may be made to the above embodiments.
As shown in fig. 3, the vehicle driving data simulation method includes the steps of:
and step 310, collecting driving data of the vehicle in different physical environments.
And step 320a, inputting the running data into a set machine learning model so as to carry out vehicle running data simulation through the set machine learning model and obtain a set number of vehicle running simulation data, wherein the set number is larger than the data amount of the running data.
And step 320b, storing the driving data to a preset distributed file system.
And 330, performing data format processing on the driving data and the vehicle driving simulation data so that the processed data format conforms to the data reading rule of the target service.
Illustratively, the performing data format processing on the running data and the vehicle running simulation data includes:
and inputting the running data and the vehicle running simulation data into an online playback module of an automobile bus development environment CANoe, and carrying out data format processing on the running data and the vehicle running simulation data through the online playback module of the CANoe.
The CANoe is a bus development environment designed by Vector company in Germany for the development of an automobile bus, is called as CAN open environment, and CAN simulate the uploading of signals generated by automobile parts. In this embodiment, the on-line playback module of the CANoe puts the corresponding signals (e.g., signal name-battery temperature, signal value-70, and signal collection time) at the corresponding positions in the table, so that the target service to be tested subsequently reads directly, instead of the target service receiving the test data, the target service preferentially performs format processing on the test data, and then starts testing, performs format processing on the test data in advance, which is helpful for improving the test efficiency of the target service. The line playback module Replay Block CAN CAN read Canlog files (mainly comprising vehicle signal names, signal values and signal collection time), simulate the behavior of vehicle parts generating signals, and then collect and distribute the simulated signals to target services through a vehicle signal analysis service interface.
Specifically, the sending the driving data and the vehicle driving simulation data to a target service includes:
and sending the running data and the vehicle running simulation data to a target service through an online playback module of the CANoe.
And 340, sending the driving data and the vehicle driving simulation data after data format processing to a target service, so that the target service performs a service performance test based on the driving data and the vehicle driving simulation data, and outputting an analysis report based on a test log.
And 350, receiving an analysis report returned by the service, and distributing an application scene for the running data and the vehicle running simulation data according to the analysis report.
Further, referring to the schematic diagram of the analysis framework of the vehicle driving data simulation based on the machine learning technology shown in fig. 4, the driving data of the vehicle in the environments of low temperature, high temperature, weak network, no network, plateau, basin and the like is collected through a vehicle data collection system (including a large number of image sensors, sound sensors, beidou systems, millimeter wave radars and the like), and the driving data includes, but is not limited to, battery temperature, battery current intensity, four-wheel tire pressure, motor speed, compressor speed, tire lateral acceleration, battery and fuel consumption and the like, so as to provide data samples for the next training of the machine learning model. Storing the collected driving data into an HDFS (high-density data processing), inputting a signal name, signal value data and signal collection time into simulation data of a test vehicle for reporting through a CANoe (computer aided design) according to the driving data in the HDFS, transmitting the driving data into an initial machine learning model for training, and obtaining a nonlinear relation of the signal data (power battery capacity, battery current, voltage, maximum and minimum battery power and the like) related to batteries in different driving environments through a PNGV (Power battery maximum value and minimum value) equivalent circuit model so as to simulate extreme values, median values and the like of the battery signal data in different environments; or assigning the corresponding relation between the generation time and the acquisition time of the same batch and different batches of signals under different network environments to other signals for simulation. The trained machine learning model can automatically generate a large amount of high simulation data (including signal names, signal values and time relations among signals), and simultaneously, the data such as the signal names, the signal values and the signal generation time are input to the data collection and report of each part of the simulated vehicle of the test vehicle through the online playback module of the CANoe. Each service records unusual and wrong scene problems through keyword analysis (such as interface return status codes, behavior occurrence timestamps, code capture exceptions and the like) of the service log, outputs an analysis report, and simultaneously transmits the batch of reported simulation data back to the HDFS to enter the iterative training of the next turbine learning model. And the whole set of framework is always in operation, the training data set of the machine learning model is continuously corrected according to the analysis report and the on-line performance of the target service in the operation process, and the machine learning model is subjected to iterative training based on the corrected training data set, so that the performance of the service obtained by testing the vehicle running simulation data output by the machine learning model after iterative training reaches a target value. And storing the corrected training data set to a preset distributed file system (HDFS) so as to read the corrected training data set when iterative training needs to be carried out on a set machine learning model later. And real scene data simulation is provided for online service, and problems are found and solved in advance.
The technical scheme of the embodiment uses a vehicle data acquisition system and a model training technology of machine learning. The vehicle data acquisition system collects south-north temperature difference data and data of the tunnel weak net without a network, and synchronously trains the machine learning model. The model outputs a large amount of simulation data of the driving data in the real environment during training, and the problems of performance test data distortion and insufficient test data amount before service online are solved. By providing massive data for the service, the service can quickly track and locate problems. By collecting data of the real driving environment (mountain rivers, tunnels and the like) of the vehicle, a model capable of automatically simulating the driving data of the vehicle is trained by machine learning, a large amount of data are guaranteed to be used for performance testing before the service is on line, and pain points which cannot be quickly tested due to the fact that the service lacks test data are solved.
According to the technical scheme of the embodiment, on the basis of the embodiment, before the running data and the vehicle running simulation data are sent to the target service, an operation of processing the data format of the running data and the vehicle running simulation data is added, so that the processed data format accords with the data reading rule of the target service, the target service can directly read the running data and the vehicle running simulation data, and then the performance test is carried out, the running data and the vehicle running simulation data do not need to be processed again, and the efficiency of testing the service performance is improved.
Example four
Fig. 5 is a vehicle driving data simulation apparatus according to a fourth embodiment of the present disclosure, the apparatus including: an acquisition module 510 and an acquisition module 520.
The acquisition module 510 is configured to acquire driving data of a vehicle in different physical environments; an obtaining module 520, configured to input the driving data into a set machine learning model, so as to perform vehicle driving data simulation through the set machine learning model, and obtain a set number of vehicle driving simulation data, where the set number is greater than a data amount of the driving data.
On the basis of the above technical solution, the collecting module 510 specifically collects the driving data of the vehicle in different physical environments through the vehicle-mounted sensor.
On the basis of the above technical solutions, the vehicle-mounted sensor includes at least one of:
image sensor, sound sensor, radar sensor and big dipper positioning sensor.
On the basis of the above technical solutions, the driving data includes at least one of the following: battery temperature, battery amperage, four-wheel tire pressure, motor speed, compressor speed, tire lateral acceleration, battery charge consumption, and fuel consumption.
On the basis of the above technical solutions, the vehicle driving data simulation apparatus further includes:
and the storage module is used for storing the running data to a preset distributed file system after the running data of the vehicle under different physical environments is collected.
On the basis of the above technical solutions, the preset distributed file system includes: the distributed file system HDFS sending module is used for sending the running data and the vehicle running simulation data to a target service so that the target service can perform service performance test based on the running data and the vehicle running simulation data and output an analysis report based on a test log;
the receiving module is used for receiving the analysis report returned by the service;
and the distribution module is used for distributing application scenes to the running data and the vehicle running simulation data according to the analysis report.
On the basis of the above technical solutions, the analysis module includes:
the identification unit is used for identifying preset key fields in the test log;
the determining unit is used for determining the corresponding error meaning of the preset key field;
a generating unit, which is used for generating an analysis report according to the error meaning;
and the output unit is used for outputting the analysis report to a processing platform matched with the wrong meaning.
On the basis of the above technical solutions, the vehicle driving data simulation apparatus further includes:
and the correction module is used for correcting the training data set of the machine learning model according to the analysis report and the on-line performance of the target service, and performing iterative training on the machine learning model based on the corrected training data set so as to enable the performance of the service obtained by testing the vehicle driving simulation data output by the machine learning model after iterative training to reach a target value.
On the basis of the above technical solutions, the correction module includes:
a first correction unit, configured to, if the analysis report shows that the test passes and the on-line performance of the target service meets a set standard, take the driving data and the vehicle driving simulation data as a corrected training data set;
and the second correction unit is used for adding a new sample on the basis of the running data and the vehicle running simulation data based on a set strategy as a corrected training data set if the analysis report shows that the test is passed and the on-line performance of the target service does not meet a set standard.
On the basis of the above technical solutions, the vehicle driving data simulation apparatus further includes:
and the processing module is used for carrying out data format processing on the running data and the vehicle running simulation data before sending the running data and the vehicle running simulation data to a target service so as to enable the processed data format to accord with the data reading rule of the target service.
On the basis of the above technical solutions, the processing module specifically inputs the driving data and the vehicle driving simulation data to an online playback module of an automobile bus development environment CANoe, so as to perform data format processing on the driving data and the vehicle driving simulation data through the online playback module of the CANoe.
On the basis of the above technical solutions, the sending module sends the driving data and the vehicle driving simulation data to a target service through the CANoe online playback module.
According to the technical scheme of the embodiment of the disclosure, more vehicle running simulation data are generated based on the collected running data of the vehicle in the real environment by means of the machine learning model, high simulation and batch generation of the vehicle running simulation data are realized, the data simulation efficiency is improved, the labor cost is reduced, sufficient high-quality data are provided for the performance test of the target service, the problem of distortion of the test data required by the performance test before the target service is on line is solved, and meanwhile, the fault problem of the vehicle is favorably and quickly positioned by the service by providing massive high-simulation running data for the service.
The vehicle running data simulation device provided by the embodiment of the disclosure can execute the vehicle running data simulation method provided by any embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the embodiments of the present disclosure.
EXAMPLE five
Referring now to fig. 6, a schematic diagram of an electronic device (e.g., the terminal device or the server in fig. 6) 400 suitable for implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), etc., and a stationary terminal such as a digital TV, a desktop computer, etc. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 400 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 6 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
The terminal provided by the embodiment of the disclosure and the vehicle driving data simulation method provided by the embodiment belong to the same inventive concept, technical details which are not described in detail in the embodiment of the disclosure can be referred to the embodiment, and the embodiment of the disclosure have the same beneficial effects.
EXAMPLE six
The disclosed embodiments provide a computer storage medium having stored thereon a computer program that, when executed by a processor, implements the vehicle travel data simulation method provided by the above-described embodiments.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
collecting driving data of a vehicle in different physical environments;
and inputting the running data into a set machine learning model to perform vehicle running data simulation through the set machine learning model to obtain a set number of vehicle running simulation data, wherein the set number is larger than the data amount of the running data.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a cell does not in some cases constitute a limitation on the cell itself, for example, an editable content display cell may also be described as an "editing cell".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (16)
1. A vehicle travel data simulation method, characterized by comprising:
collecting driving data of a vehicle in different physical environments;
and inputting the running data into a set machine learning model to perform vehicle running data simulation through the set machine learning model to obtain a set number of vehicle running simulation data, wherein the set number is larger than the data amount of the running data.
2. The method of claim 1, wherein the collecting driving data of the vehicle in different physical environments comprises:
the driving data of the vehicle under different physical environments is acquired through the vehicle-mounted sensor.
3. The method of claim 2, wherein the onboard sensors comprise at least one of:
image sensor, sound sensor, radar sensor and big dipper positioning sensor.
4. The method of claim 1, wherein the travel data comprises at least one of: battery temperature, battery amperage, four-wheel tire pressure, motor speed, compressor speed, tire lateral acceleration, battery charge consumption, and fuel consumption.
5. The method according to any one of claims 1-4, wherein after collecting the driving data of the vehicle in different physical environments, the method further comprises:
and storing the driving data to a preset distributed file system.
6. The method of claim 5, wherein the provisioning of the distributed file system comprises: distributed file system HDFS.
7. The method according to any one of claims 1-4, further comprising:
sending the running data and the vehicle running simulation data to a target service so that the target service performs a service performance test based on the running data and the vehicle running simulation data and outputs an analysis report based on a test log;
receiving an analysis report returned by the service;
and distributing application scenes for the running data and the vehicle running simulation data according to the analysis report.
8. The method of claim 7, wherein outputting an analysis report based on the test log comprises:
identifying preset key fields in the test log;
determining the error meaning corresponding to the preset key field;
generating an analysis report according to the error meaning;
and outputting the analysis report to a processing platform matched with the wrong meaning.
9. The method of claim 7, further comprising:
and correcting the training data set of the machine learning model according to the analysis report and the on-line performance of the target service, and performing iterative training on the machine learning model based on the corrected training data set so as to enable the performance of the service obtained by testing the vehicle driving simulation data output by the machine learning model after iterative training to reach a target value.
10. The method of claim 9, wherein modifying the training data set of the machine learning model based on the analysis report and an online performance of a target service comprises:
if the analysis report shows that the test is passed and the on-line performance of the target service reaches a set standard, taking the running data and the vehicle running simulation data as a corrected training data set;
and if the analysis report shows that the test is passed and the on-line performance of the target service does not meet the set standard, adding a new sample as a corrected training data set on the basis of the running data and the vehicle running simulation data based on a set strategy.
11. The method of claim 7, wherein prior to sending the driving data and the vehicle driving simulation data to a target service, further comprising:
and carrying out data format processing on the running data and the vehicle running simulation data so as to enable the processed data format to accord with the data reading rule of the target service.
12. The method of claim 11, wherein said data formatting said driving data and said vehicle driving simulation data comprises:
and inputting the running data and the vehicle running simulation data into an online playback module of an automobile bus development environment CANoe, and carrying out data format processing on the running data and the vehicle running simulation data through the online playback module of the CANoe.
13. The method of claim 12, wherein sending the driving data and the vehicle driving simulation data to a target service comprises:
and sending the running data and the vehicle running simulation data to a target service through an online playback module of the CANoe.
14. A vehicle travel data simulation apparatus, characterized by comprising:
the acquisition module is used for acquiring running data of the vehicle in different physical environments;
the acquisition module is used for inputting the driving data into a set machine learning model so as to carry out vehicle driving data simulation through the set machine learning model and obtain a set number of vehicle driving simulation data, wherein the set number is larger than the data volume of the driving data.
15. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the vehicle travel data simulation method of any of claims 1-13.
16. A storage medium containing computer-executable instructions for performing the vehicle travel data simulation method of any one of claims 1-13 when executed by a computer processor.
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