CN112116508A - Method and related device for judging driving training weak items - Google Patents
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Abstract
The embodiment of the application discloses a method for judging weak items of driving training and a related device, which are used for improving the training precision of training personnel and improving the training efficiency. The method in the embodiment of the application comprises the following steps: acquiring driving state information of a target vehicle in driving training; classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items; uploading the driving project information to a cloud database, wherein the cloud database is used for storing the driving project information and is a driving training data center; carrying out multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model to generate driving comparison data of a driver of a corresponding target vehicle; and generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
Description
Technical Field
The embodiment of the application relates to the field of vehicle driving capability evaluation, in particular to a method for judging weak driving training items and a related device.
Background
For vehicle driving, a driver needs a lot of basic driving training from the beginning of learning to drive the vehicle to the time of possessing a driver's license, and a driving coach usually guides the trainee in driving ability.
The traditional method for identifying the weak items of the trainees is that the trainees judge the weak items of the trainees artificially according to self visual observation and perception, and more attention is paid to success and failure of subject practice. The method is relatively deviated, lacks data support, is closely related to the experience of a coach, and is easy to cause deviation or inaccurate weak item reminding or guidance for a student.
In general, the traditional method for judging the driving training weakness has great uncertainty, reduces the training precision of training personnel, and reduces the training efficiency.
Disclosure of Invention
The embodiment of the application discloses a method for judging weak items of driving training and a related device, which are used for improving the training precision of training personnel and improving the training efficiency.
The embodiment of the application provides a method for judging weak items of driving training in a first aspect, which comprises the following steps:
acquiring driving state information of a target vehicle in driving training, wherein the driving state information comprises but is not limited to running state data of all parts of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
uploading the driving project information to a cloud database, wherein the cloud database is used for storing the driving project information and is a driving training data center;
carrying out multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents comparison between item training degree results of the driver at each stage in a driving training process and public data;
and generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
Optionally, the acquiring the driving state information of the target vehicle in the driving training includes:
acquiring running state data of each component of a target vehicle through an intelligent sensor;
acquiring operation behavior data of a driver through a camera;
and acquiring field feedback data through a field sensor.
Optionally, the generating and outputting the driving ability weakness item evaluation according to the driving comparison data includes:
generating driving capacity weakness item evaluation according to the driving comparison data;
and sending the driving capacity weakness item evaluation to an on-board device on the target vehicle so that the driver is guided and corrected in real time.
Optionally, after the sending of the driving performance impairment evaluation to the on-board device on the target vehicle, the method further comprises:
sending the driving ability impairment evaluation to authorized driving perimeter related platforms including, but not limited to, a driver platform, a traffic platform, and a driving training platform.
Optionally, the classifying the driving state information according to the site feedback data in the driving state information to generate driving item information of different training items includes:
determining a current training project according to the field feedback data;
and classifying the driving state information according to the training items to generate driving item information of different training items.
A second aspect of the embodiments of the present application provides an apparatus for determining a driving training weakness, including:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring driving state information of a target vehicle in driving training, and the driving state information comprises but is not limited to running state data of all components of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
the first generation unit is used for classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
the uploading unit is used for uploading the driving project information to a cloud database, the cloud database is used for storing the driving project information, and the cloud database is a driving training data center;
the second generation unit is used for carrying out multi-dimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model so as to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents comparison between item training degree results of the driver at each stage in the driving training process and public data;
and the third generating unit is used for generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
Optionally, the first obtaining unit includes:
the second acquisition module is used for acquiring the running state data of each component of the target vehicle through the intelligent sensor;
the third acquisition module is used for acquiring the operation behavior data of the driver through the camera;
and the fourth acquisition module is used for acquiring field feedback data through the field sensor.
Optionally, the third generating unit includes:
the fourth generation module is used for generating driving capacity weak item evaluation according to the driving comparison data;
and the first sending module is used for sending the driving capacity weakness item evaluation to an on-board device on the target vehicle so that the driver can be guided and corrected in real time.
Optionally, the apparatus further comprises:
and the second sending unit is used for sending the driving ability weakness evaluation to authorized driving periphery related platforms, wherein the authorized driving periphery related platforms comprise but are not limited to a driver platform, a traffic platform and a driving training platform.
Optionally, the first generating unit includes:
the determining module is used for determining the current training item according to the field feedback data;
and the fifth generation module is used for classifying the driving state information according to the training items so as to generate driving item information of different training items.
A third aspect of the embodiments of the present application provides an apparatus for determining a driving training weakness, including:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the processor specifically performs the following operations:
acquiring driving state information of a target vehicle in driving training, wherein the driving state information comprises but is not limited to running state data of all parts of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
uploading the driving project information to a cloud database, wherein the cloud database is used for storing the driving project information and is a driving training data center;
carrying out multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents comparison between item training degree results of the driver at each stage in a driving training process and public data;
and generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
Optionally, the processor is further configured to perform the operations of any of the alternatives of the first aspect.
A computer readable storage medium having a program stored thereon, the program, when executed on a computer, performing the method of the first aspect as well as any of the alternatives of the first aspect.
According to the technical scheme, the embodiment of the application has the following advantages:
the method comprises the steps of firstly, obtaining driving state information of a target vehicle in driving training, and classifying the driving state information according to field feedback data in the driving state information to generate driving item information of different training items. The driving project information is uploaded to a cloud database, the cloud database is used for storing the driving project information, then multidimensional big data learning and analysis are carried out on the driving project information in the cloud database by using an artificial intelligence model so as to generate driving comparison data of a driver of a corresponding target vehicle, and finally driving weak item evaluation is generated and output according to the driving comparison data. Big data analysis is carried out on the operation of each driver in the training process through a big data artificial intelligence model, the degree to which the training items of the driver in the stage are required to reach and the actual reaching degree are judged, so that the weak items are judged, corresponding evaluation is carried out on the weak items, the training precision of training personnel is improved, and the training efficiency is improved.
Drawings
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for determining driving training weaknesses in an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating an embodiment of a method for determining driving training weaknesses according to the present disclosure;
FIG. 3 is a schematic structural diagram of an embodiment of an apparatus for determining driving training weaknesses in an embodiment of the present application;
FIG. 4 is a schematic structural diagram of another embodiment of an apparatus for determining driving training weakness in the embodiment of the present application;
fig. 5 is a schematic structural diagram of another embodiment of the device for determining driving training weakness in the embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of the present invention.
The embodiment of the application discloses a method for judging weak items of driving training and a related device, which are used for improving the training precision of training personnel and improving the training efficiency.
In this embodiment, the method for determining the driving training weakness can be implemented in a system, a server, or a terminal, and is not specifically limited. For convenience of description, the embodiment of the present application uses the system as an example for the execution subject.
Referring to fig. 1, an embodiment of a method for determining driving training weakness in an embodiment of the present application includes:
101. the method comprises the steps that a system obtains driving state information of a target vehicle in driving training, wherein the driving state information comprises but is not limited to running state data of all parts of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
the system acquires driving state information generated by the vehicle in the driving process so as to obtain the operation behavior of the vehicle driver for driving the vehicle and the action of the vehicle on the operation behavior of the driver. The driving state information may include various types, such as operation video image information when the driver drives the vehicle, data information of each vehicle during the operation of the vehicle, image information of the surrounding environment, and the like, and is not limited herein. It should be noted that the collected information includes, but is not limited to, steering wheel operation information, clutch pedal operation information, foot brake pedal operation information, hand brake operation information, turn signal operation information, accelerator pedal operation information, and information related to safe driving behavior habits, such as the head pose, the sight line, the two-hand movement, the upper limb and trunk movement, the getting-on and getting-off movement, the safety belt fastening movement, and the door driving movement of the driver.
The driving state information may be obtained in various manners, such as by shooting the operation of the driver in the vehicle with a built-in camera, by scanning the operation of the driver with a thermal imaging system, by measuring the degree of tightness of the accelerator and the brake of the vehicle with a sensor, or by determining the environmental information of the current environment with a GPS system, which is not limited herein.
The venue feedback data may be training data fed back by sensors of a training venue, such as: the loan population edge pressure data is used for measuring whether the vehicle presses a line or not in the process of backing and warehousing; infrared data, which is used to determine whether the warehousing location is accurate, etc., and is not limited here.
102. The system classifies the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
the system classifies the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items. And determining the data of a certain section as the data of which training item according to the positioning point information in the feedback data of different fields.
103. The system uploads the driving project information to a cloud database, the cloud database is used for storing the driving project information, and the cloud database is a driving training data center;
the system uploads the driving item information to the cloud, so that other operations such as other system calls, queries and the like can be performed on the data after the data are networked, and the system is not limited here. After the system generates the real-time driving behavior data through the target safety model, historical comparison needs to be carried out on the real-time driving behavior data, and information of such driving items needs to be uploaded to a cloud database, so that multiple data are compared, and accurate analysis data are obtained.
The cloud database is a storage type database, and in the embodiment, the cloud database is mainly used for storing the driving item information of all vehicles, the driving item information is accumulated in the daily and monthly process, and the driving capability of an individual or all drivers can be analyzed and stored.
104. The system uses an artificial intelligent model to carry out multidimensional big data learning and analysis on the driving project information in the cloud database so as to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents the comparison between the project training degree result of the driver at each stage in the driving training process and public data;
the system uses the artificial intelligence model to analyze all data in the cloud database, and then obtains a global data analysis result, for example: the accuracy of the week student in backing and warehousing is ten percent higher than that of the week student in the previous period, the occurrence frequency of dangerous behaviors in the driving process of the day is twenty percent higher than that of yesterday, and the method is not limited here. And the individual driving ability process data can be obtained, specific individual behavior data and group driving ability data are analyzed, and the driving ability process is evaluated according to an evaluation model by combining the result, the driving ability, the safe driving consciousness and the safe driving behavior in the driving process. For example: the student's own week backing and warehousing success rate is twenty percent higher than the group success rate, and is improved compared with the previous week. The dangerous operation is 14 times, and the total dangerous operation is gradually reduced.
The artificial intelligence calculation model can analyze and calculate the metadata according to different data latitudes to form a huge data middle stage through self strong calculation capability, trillion-level data processing millisecond-level calculation response is realized, and near-real-time data support is provided. For example: and analyzing the success rate of backing and warehousing under different training fields so as to analyze whether information that the driving training effect is influenced due to field factors exists.
105. And the system generates and outputs driving ability weak item evaluation according to the driving comparison data.
The system generates a driving ability process evaluation for each driver based on the driving ability process data, including analysis and comparison of the operation, and outputs the driving ability process evaluation to the whole system or platform, so that the evaluation is utilized by the whole system.
Optionally, in this embodiment, the procedural evaluation may be transmitted to the driver, and the driver may view the own driving ability procedural evaluation feedback in real time through a mobile terminal including, but not limited to, a terminal such as a smart phone, a public account, an applet, and the like, so as to improve the driving ability, standardize the driving behavior, and ensure the safety of the driver. And (3) the driver makes up the insufficient driving ability according to the process evaluation feedback of the driving ability, corrects the wrong driving behavior, and corrects the wrong driving behavior in the later driving process to form a forward driving ability process evaluation closed loop.
In this embodiment, first, driving state information of a target vehicle in driving training is obtained, and the driving state information is classified according to field feedback data in the driving state information, so as to generate driving item information of different training items. The driving project information is uploaded to a cloud database, the cloud database is used for storing the driving project information, then multidimensional big data learning and analysis are carried out on the driving project information in the cloud database by using an artificial intelligence model so as to generate driving comparison data of a driver of a corresponding target vehicle, and finally driving weak item evaluation is generated and output according to the driving comparison data. Big data analysis is carried out on the operation of each driver in the training process through a big data artificial intelligence model, the degree to which the training items of the driver in the stage are required to reach and the actual reaching degree are judged, so that the weak items are judged, corresponding evaluation is carried out on the weak items, the training precision of training personnel is improved, and the training efficiency is improved.
Referring to fig. 2, another embodiment of the method for determining driving training weakness in the embodiment of the present application includes:
201. the system acquires the running state data of each component of the target vehicle through an intelligent sensor;
the system acquires the running state data of each component of the target vehicle through the intelligent sensor, and acquires each data under the running state of the automobile through the sensor, wherein the data include but are not limited to: steering wheel operation information, clutch pedal operation information, foot brake pedal operation information, hand brake operation information, turn signal operation information, and accelerator pedal operation information, which are not limited herein.
202. The system acquires the operation behavior data of a driver through a camera;
the action video of the driver in the driving process is collected through the camera, and all operation behavior information of the driver is obtained from different directions.
203. The system acquires site feedback data through a site sensor;
the system obtains the site feedback data through the site sensor, and the site feedback data can be the training data fed back by the sensor in the training site, for example: the loan population edge pressure data is used for measuring whether the vehicle presses a line or not in the process of backing and warehousing; infrared data, which is used to determine whether the warehousing location is accurate, etc., and is not limited here.
204. The system determines a current training project according to the field feedback data;
the system determines a current training project according to field feedback data, and judges the training time of a current vehicle in a certain project training field according to GPS system information in the field feedback data, wherein the operation data and the vehicle data in the time period are the data of the training project.
205. The system classifies the driving state information according to the training items to generate driving item information of different training items;
the system classifies the driving state information according to the training items, classifies data of different training items, and compares the classified data with data of other training items according to big data.
206. The system uploads the driving project information to a cloud database, the cloud database is used for storing the driving project information, and the cloud database is a driving training data center;
207. the system uses an artificial intelligent model to carry out multidimensional big data learning and analysis on the driving project information in the cloud database so as to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents the comparison between the project training degree result of the driver at each stage in the driving training process and public data;
208. The system generates driving capacity weakness item evaluation according to the driving comparison data;
and generating driving capacity weak item evaluation according to the driving comparison data, comparing the data of the driver with the data of other drivers, and then obtaining driving capacity weak item evaluation content aiming at the evaluation table.
209. The system sends the driving capacity weakness item evaluation to an on-board device on the target vehicle so that the driver can be guided and corrected in real time;
and the system sends the driving ability weak item evaluation to the vehicle-mounted device on the target vehicle, namely, the current training result is fed back to the driver in real time.
210. The system sends the driving ability impairment evaluation to authorized driving perimeter related platforms including, but not limited to, driver platforms, traffic platforms, and driving training platforms.
Optionally, in this embodiment, the system records the driving ability process of the driver from the driving training, and continuously performs evaluation and result feedback on the driving ability process of the driver, and enables related parties including but not limited to driving training, traffic control centers, traffic related departments, vehicle insurance, credit reporting systems, vehicle maintenance, and the like. The purpose is to lead the drivers to restrict the driving behaviors of the drivers in the daily safe driving process and continuously accumulate the correct safe driving regulations.
The method comprises the steps of firstly, obtaining driving state information of a target vehicle in driving training, and classifying the driving state information according to field feedback data in the driving state information to generate driving item information of different training items. The driving project information is uploaded to a cloud database, the cloud database is used for storing the driving project information, then multidimensional big data learning and analysis are carried out on the driving project information in the cloud database by using an artificial intelligence model so as to generate driving comparison data of a driver of a corresponding target vehicle, and finally driving weak item evaluation is generated and output according to the driving comparison data. Big data analysis is carried out on the operation of each driver in the training process through a big data artificial intelligence model, the degree to which the training items of the driver in the stage are required to reach and the actual reaching degree are judged, so that the weak items are judged, corresponding evaluation is carried out on the weak items, the training precision of training personnel is improved, and the training efficiency is improved.
Second, by sending the evaluations to different enabling platforms, training data is fully utilized.
Referring to fig. 3, an embodiment of the apparatus for determining driving training weakness in the embodiment of the present application includes:
a first obtaining unit 301, configured to obtain driving state information of a target vehicle in driving training, where the driving state information includes, but is not limited to, operation state data of each component of the target vehicle in a current driving process, operation behavior data of a driver, and site feedback data;
a first generating unit 302, configured to classify the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, where the driving item information is training data of a certain training item in the current training of the driver;
the uploading unit 303 is configured to upload the driving item information to a cloud database, where the cloud database is used to store the driving item information, and the cloud database is a driving training data center;
a second generating unit 304, configured to perform multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligence model, so as to generate driving comparison data of a driver of a corresponding target vehicle, where the driving comparison data represents a comparison between a training degree result of the driver at each stage in a driving training process and public data;
and a third generating unit 305, configured to generate and output an evaluation of the driving performance weakness item according to the driving comparison data.
Referring to fig. 4, another embodiment of the apparatus for determining driving training weakness in the embodiment of the present application includes:
a first obtaining unit 401, configured to obtain driving state information of a target vehicle in driving training, where the driving state information includes, but is not limited to, operation state data of each component of the target vehicle in a current driving process, operation behavior data of a driver, and site feedback data;
in this embodiment, the first obtaining unit 401 includes a second obtaining module 4011, a third obtaining module 4012, and a fourth obtaining module 4013.
The second obtaining module 4011 is configured to obtain operating state data of each component of the target vehicle through the intelligent sensor;
the third obtaining module 4012 is configured to obtain operation behavior data of the driver through the camera;
the fourth obtaining module 4013 is configured to obtain venue feedback data through a venue sensor;
a first generating unit 402, configured to classify the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, where the driving item information is training data of a certain training item in the current training of the driver;
in this embodiment, the first generating unit 402 includes a determining module 4021 and a fifth generating module 4022.
A determining module 4021, configured to determine a current training item according to the venue feedback data;
a fifth generating module 4022, configured to classify the driving state information according to the training items to generate driving item information of different training items;
an uploading unit 403, configured to upload the driving item information to a cloud database, where the cloud database is used to store the driving item information, and the cloud database is a driving training data center;
a second generating unit 404, configured to perform multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligence model to generate driving comparison data of a driver of a corresponding target vehicle, where the driving comparison data represents a comparison between a training degree result of the driver at each stage in a driving training process and public data;
a third generating unit 405, configured to generate and output an evaluation of a driving performance weakness item according to the driving comparison data;
in this embodiment, the third generating unit 405 includes a fourth generating module 4051 and a second sending unit 406.
A fourth generating module 4051, configured to generate a driving ability weakness item evaluation according to the driving comparison data;
a first sending module 4052, configured to send the driving ability weakness evaluation to an on-board device on the target vehicle, so that the driver is guided and corrected in real time;
a second sending unit 406, configured to send the driving ability weakness evaluation to authorized driving periphery-related platforms, including but not limited to a driver platform, a transportation platform, and a driving training platform.
Referring to fig. 5, another embodiment of the apparatus for determining driving training weakness in the embodiment of the present application includes:
a processor 501, a memory 502, an input/output unit 503, and a bus 504;
the processor 501 is connected with the memory 502, the input/output unit 503 and the bus 504;
the processor 501 specifically performs the following operations:
acquiring driving state information of a target vehicle in driving training, wherein the driving state information comprises but is not limited to running state data of all parts of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
uploading the driving project information to a cloud database, wherein the cloud database is used for storing the driving project information and is a driving training data center;
carrying out multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents comparison between item training degree results of the driver at each stage in a driving training process and public data;
and generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
In this embodiment, the functions of the processor 501 correspond to the steps in the embodiments shown in fig. 1 to fig. 2, and are not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A method of determining driving training weaknesses, comprising:
acquiring driving state information of a target vehicle in driving training, wherein the driving state information comprises but is not limited to running state data of all parts of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
uploading the driving project information to a cloud database, wherein the cloud database is used for storing the driving project information and is a driving training data center;
carrying out multidimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents comparison between item training degree results of the driver at each stage in a driving training process and public data;
and generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
2. The method of claim 1, wherein the obtaining driving state information of the target vehicle in the driving training comprises:
acquiring running state data of each component of a target vehicle through an intelligent sensor;
acquiring operation behavior data of a driver through a camera;
and acquiring field feedback data through a field sensor.
3. The method of claim 1, wherein generating and outputting driving performance weakness assessment from the driving comparison data comprises:
generating driving capacity weakness item evaluation according to the driving comparison data;
and sending the driving capacity weakness item evaluation to an on-board device on the target vehicle so that the driver is guided and corrected in real time.
4. The method of claim 3, wherein after the sending of the drivability deficiency evaluation to the onboard device on the target vehicle, the method further comprises:
sending the driving ability impairment evaluation to authorized driving perimeter related platforms including, but not limited to, a driver platform, a traffic platform, and a driving training platform.
5. The method of any one of claims 1 to 4, wherein the classifying the driving state information according to the venue feedback data in the driving state information to generate driving item information for different training items comprises:
determining a current training project according to the field feedback data;
and classifying the driving state information according to the training items to generate driving item information of different training items.
6. An apparatus for determining a driving training weakness, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring driving state information of a target vehicle in driving training, and the driving state information comprises but is not limited to running state data of all components of the target vehicle, operation behavior data of a driver and site feedback data in the current driving process;
the first generation unit is used for classifying the driving state information according to the field feedback data in the driving state information to generate driving item information of different training items, wherein the driving item information is training data of a certain training item in the current training of the driver;
the uploading unit is used for uploading the driving project information to a cloud database, the cloud database is used for storing the driving project information, and the cloud database is a driving training data center;
the second generation unit is used for carrying out multi-dimensional big data learning and analysis on the driving item information in the cloud database by using an artificial intelligent model so as to generate driving comparison data of a driver of a corresponding target vehicle, wherein the driving comparison data represents comparison between item training degree results of the driver at each stage in the driving training process and public data;
and the third generating unit is used for generating and outputting driving capacity weakness item evaluation according to the driving comparison data.
7. The apparatus of claim 6, wherein the first obtaining unit comprises:
the second acquisition module is used for acquiring the running state data of each component of the target vehicle through the intelligent sensor;
the third acquisition module is used for acquiring the operation behavior data of the driver through the camera;
and the fourth acquisition module is used for acquiring field feedback data through the field sensor.
8. The apparatus of claim 6, wherein the third generating unit comprises:
the fourth generation module is used for generating driving capacity weak item evaluation according to the driving comparison data;
and the first sending module is used for sending the driving capacity weakness item evaluation to an on-board device on the target vehicle so that the driver can be guided and corrected in real time.
9. The apparatus of claim 8, further comprising:
and the second sending unit is used for sending the driving ability weakness evaluation to authorized driving periphery related platforms, wherein the authorized driving periphery related platforms comprise but are not limited to a driver platform, a traffic platform and a driving training platform.
10. The apparatus according to any one of claims 6 to 9, wherein the first generating unit comprises:
the determining module is used for determining the current training item according to the field feedback data;
and the fifth generation module is used for classifying the driving state information according to the training items so as to generate driving item information of different training items.
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