CN110544409A - personalized driver training system and teaching method thereof - Google Patents
personalized driver training system and teaching method thereof Download PDFInfo
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Abstract
The invention provides an individualized driver training system and a teaching method thereof by utilizing an Internet of things sensor, an image processing technology and an artificial intelligence technology. Through giving the driver label, according to label distribution teaching plan, sensor and image processing module in the edge calculation module of individualized driver training system carry out position capture such as hand, eye to the driver in the training process, remind and guide to correct unreasonable action, utilize high in the clouds calculation module to reach the high in the clouds analysis with driver training record and training orbit and become suitable, efficient teaching plan and optimize benchmark teaching plan, the driver can look over own training information from student's cell-phone end, coach and look over driver's training record from coach cell-phone end, the examination score, can comment on training at every turn, look over driver error statistics etc.. The invention can improve the learning efficiency in driver training, is beneficial to improving the examination passing rate and shortening the teaching time.
Description
Technical Field
The invention relates to the technical field of Internet of things and artificial intelligence, in particular to an individualized driver training system.
Background
The existing driver training adopts manual one-to-one belt teaching of a coach, and the coach remembers the characteristics of each driver, including physical characteristics such as sitting posture and eye height and motion characteristics such as body coordination capacity and reaction speed, mastery degree and examination characteristics such as stability of performance.
aiming at the characteristics, a coach can manually match the teaching contents, methods and means (hereinafter referred to as teaching plan) suitable for the driver, the communication between the coach and the coach is difficult, the communication between the coach and the driver is difficult, and the coach can be easily confused when carrying a plurality of drivers. Therefore, drivers learn the vehicle from the beginning to the end of the coach at present.
Meanwhile, due to the restriction of the limiting conditions, although the examination standards are unified, the same teaching standard is difficult to use to require each teaching activity, and the expected teaching time is not enough or wasted, so that the production shift of a driving school unit is adversely affected.
disclosure of Invention
the invention aims to overcome the defects of the prior art, and provides a personalized driver training system and a teaching method thereof, which can efficiently and intelligently identify various motion characteristics of a driver and formulate a specific teaching plan according to the motion characteristics by utilizing an internet of things sensor, an image processing technology and an artificial intelligence technology.
in order to achieve the above object, the present invention provides an individualized driver training system, which includes a sensor module a, an edge calculation module B, a cloud calculation module C, and a user terminal module D:
The sensor module A comprises a driver identity authentication sensor A1, a camera A2, a vehicle state sensor A3 and a judgment deduction sensor A4; the A1 driver identity authentication sensor carries out identity verification on a driver, allocates an initial label containing the height and the age of the driver, and generates a matched initial teaching plan through the initial label; the A2 driver takes photos at regular time when shooting the camera for training, and the behavior of the driver is analyzed; the A3 vehicle state sensor acquires vehicle information through a vehicle-mounted computer; the A4 judgment deduction sensor records the incorrect action of the driver in the training process;
The B edge calculation module comprises a B1 driver image processing module, a B2 judgment deduction module and a B3 current training plan adjusting module; the B1 driver image processing module collects the front data of the driver, the camera is used for dynamically capturing the eye position, the hand position and the shoulder position of the driver in the training process, a suggested adjusting scheme is given through voice, and unreasonable attention points are reminded in time; the B2 judgment and deduction module detects and receives the record of the A4 judgment and deduction sensor and then judges a deduction action; the B3 current training plan adjusting module dynamically adjusts the label of the current driver according to the action characteristics, the learning characteristics and the deduction points of the current driver, and further adjusts the current teaching plan;
The C cloud computing module comprises C1 cloud personalized teaching plan adjustment, C2 cloud vehicle and site grading adjustment and C3 cloud benchmark teaching plan adjustment; the C1 cloud personalized teaching plan is adjusted to obtain a proper and efficient training teaching plan through statistics and analysis of behavior habits of a driver during training in the cloud, and error operation and habits of the driver are gradually guided and corrected; the grading adjustment of the C2 cloud vehicle and the site enables a driver to evaluate and feed back the vehicle and the site used in training through the vehicle-mounted terminal and the D1 student mobile phone end, and a worker confirms and corrects the problem; the C3 cloud reference teaching plan is adjusted, collected and suitable for a driver coach teaching method and summarized, a plurality of sets of reference teaching plans are generated, the passing rate of the reference teaching plans is evaluated according to the training condition and the examination condition of the driver, and the reference teaching plans are adjusted and optimized;
The D user terminal module comprises a D1 student mobile phone end and a D2 coach mobile phone end; the D1 student mobile phone end can receive personalized content push of the C cloud computing module; and the D2 coach mobile phone end can receive the pushing of the learning condition of the driver of the C cloud computing module.
Furthermore, when the driver reports for the first time, the sensor module A acquires information such as sitting height and eye position, and the cloud computing module C distributes an initial label according to the information.
furthermore, in the driver training process, the sensor module A and the edge calculation module B collect the attention distribution condition, the hand-eye coordination condition, the vehicle learning score and the point deduction of the driver. The information is reported to the C cloud computing module, and after the C cloud computing module performs computing, the label of the driver is correspondingly adjusted.
Further, the A2 shooting student camera is a high-definition infrared camera and can shoot at night.
further, the a3 vehicle state sensor includes the OBD sensor, obtains vehicle information through the on-vehicle computer, like: vehicle speed, vehicle light information: dipped headlight, high beam, left turn signal, right turn signal, safety belt and door state.
further, A4 judges to deduct the sensor and comprises OBD sensor, high definition digtal camera and GPS location.
Furthermore, the B2 judges that the module of deducting uses high definition digtal camera and OBD sensor to judge whether other regulations such as the vehicle is qualified such as line ball.
Further, the adjustment schemes comprise a rearview mirror adjustment scheme and a seat adjustment scheme which are automatically given by the system and aim at the height and sitting height of the driver, and a schematic diagram of how to adjust the rearview mirror and the seat is given on a terminal screen.
Further, the personalized content pushing comprises training records, training tracks, examination requirements of all items, learning videos, examination scores and the like of the driver.
Further, the learning condition of the driver comprises training records, examination scores, error statistics of the driver and the like of the driver.
further, the personalized driver training system according to any one of claims 1 to 8 is adopted, the driver is assigned with a label by the C cloud computing module from beginning to end, the C cloud computing module organizes teaching contents according to the label of the driver, the label is from basic information of the driver, training data collected by the A sensor module and the B edge computing module, a coach and driver feedback information collected by the D user terminal module, and the teaching contents combined with the label are a personalized teaching plan which comprises teaching contents and teaching methods, such as a route for backing up, prompting contents and a prompting mode, and the teaching method of the personalized driver training system comprises the following steps:
S01, a driver fills out identity data, a student camera is shot to the A2 after getting on the bus to collect a picture, the C cloud computing module gives a seat and rearview mirror adjusting suggestion according to the filled-in data and picture analysis, and the C cloud computing module automatically distributes a label 1 to the driver, such as high, short-sighted and good in movement; according to the label 1, the C cloud computing module automatically selects a teaching plan, such as 'no correction in place';
S02, a driver learns the first project and carries out primary training under the guidance of the D user terminal module to generate a deduction item; a2 shooting a student camera to capture bad actions of a driver during training in the whole process, such as the fact that the gaze directions of eyes are not right, a neck is extended, and a probe is detected; repeating the training to generate more deduction items; c1 cloud personalized teaching plan adjustment, namely adjusting the label of a driver to be label 2 according to the deduction item, and correcting the teaching plan by the system according to the label 2;
S03, after training is finished, the driver receives the learning record at the D1 student mobile phone end, the coach receives the learning record of the driver at the D2 coach mobile phone end, and the driver and the coach can play back and comment; c1 cloud personalized teaching plan adjustment adjusting a driver label 'tag 3' according to feedback of a driver and a coach, and correcting the teaching plan according to the 'tag 3' by the system;
s04, adjusting and collecting excellent coach teaching methods by using the C3 cloud-side reference teaching plan, summarizing the excellent coach teaching methods, generating a plurality of sets of reference teaching plans, evaluating the passing rate of the reference teaching plan according to the training condition and the examination condition of a driver, and adjusting and optimizing the reference teaching plan;
and S05, in the S04 process, the C cloud computing module collects data of a driver, a coach and a training field all the time, calculates the data and adjusts a reference teaching plan.
The invention has the beneficial effects that: an individual driver training system is provided by utilizing an internet of things sensor, an image processing technology and an artificial intelligence technology, and through giving a label to a driver, according to the label distribution teaching plan, the sensor and the image processing module in the edge computing module in the personalized driver training system capture the positions of hands, eyes and the like of a driver in the training process, the intelligent training system has the advantages that unreasonable actions are reminded, guided and corrected, the training records and training tracks of a driver are uploaded to the cloud computing module to be analyzed to form a proper and efficient teaching plan and optimize a standard teaching plan, the driver can check the training records of the driver from a student mobile phone end, the training tracks, examinations of all projects need to be led, learning videos, examination scores and the like, the trainers and the training records of the driver from the trainer mobile phone end can be checked, the examination scores can be commented on each training, and error statistics of the driver and the like can be checked. The more efficient learning method is beneficial to the examination passing rate and effectively shortens the teaching time.
Drawings
For ease of illustration, the present invention is described in detail by the following preferred embodiments and the accompanying drawings.
FIG. 1 is a block diagram of a module connection relationship for a personalized driver training system and a teaching method thereof according to the present invention;
Fig. 2 is a schematic diagram of a training process of the personalized driver training system and the teaching method thereof according to the present invention.
Detailed Description
A personalized driver training system according to the invention is further described below with reference to the accompanying drawings:
As shown in fig. 1, an individualized driver training system includes a sensor module a, a B-edge calculation module, a C-cloud calculation module, and a D-user terminal module:
the sensor module A comprises a driver identity authentication sensor A1, a camera A2, a vehicle state sensor A3 and a judgment deduction sensor A4; the A1 driver identity authentication sensor carries out identity verification on a driver, allocates an initial label containing the height and the age of the driver, and generates a matched initial teaching plan through the initial label; the A2 driver takes photos at regular time when shooting the camera for training, and the behavior of the driver is analyzed; the A3 vehicle state sensor acquires vehicle information through a vehicle-mounted computer; the A4 judgment deduction sensor records the incorrect action of the driver in the training process;
the B edge calculation module comprises a B1 driver image processing module, a B2 judgment deduction module and a B3 current training plan adjusting module; the B1 driver image processing module collects the front data of the driver, the camera is used for dynamically capturing the eye position, the hand position and the shoulder position of the driver in the training process, a suggested adjusting scheme is given through voice, and unreasonable attention points are reminded in time; the B2 judgment and deduction module detects and receives the record of the A4 judgment and deduction sensor and then judges a deduction action; the B3 current training plan adjusting module dynamically adjusts the label of the current driver according to the action characteristics, the learning characteristics and the deduction points of the current driver, and further adjusts the current teaching plan;
The C cloud computing module comprises C1 cloud personalized teaching plan adjustment, C2 cloud vehicle and site grading adjustment and C3 cloud benchmark teaching plan adjustment; the C1 cloud personalized teaching plan is adjusted to obtain a proper and efficient training teaching plan through statistics and analysis of behavior habits of a driver during training in the cloud, and error operation and habits of the driver are gradually guided and corrected; the grading adjustment of the C2 cloud vehicle and the site enables a driver to evaluate and feed back the vehicle and the site used in training through the vehicle-mounted terminal and the D1 student mobile phone end, and a worker confirms and corrects the problem; the C3 cloud reference teaching plan is adjusted, collected and suitable for a driver coach teaching method and summarized, a plurality of sets of reference teaching plans are generated, the passing rate of the reference teaching plans is evaluated according to the training condition and the examination condition of the driver, and the reference teaching plans are adjusted and optimized;
the D user terminal module comprises a D1 student mobile phone end and a D2 coach mobile phone end; the D1 student mobile phone end can receive personalized content push of the C cloud computing module; and the D2 coach mobile phone end can receive the pushing of the learning condition of the driver of the C cloud computing module.
a2 shoots the student's camera and is high definition infrared camera, can shoot night.
A3 vehicle state sensor includes the OBD sensor, acquires vehicle information through on-vehicle computer, if: vehicle speed, vehicle light information: dipped headlight, high beam, left turn signal, right turn signal, safety belt and door state.
A4 judges to detain the sensor and comprises OBD sensor, high definition digtal camera and GPS location.
and the B2 judging and deducting module judges whether other test rules such as whether the vehicle is pressed are qualified or not by using a high-definition camera and an OBD sensor.
the adjusting scheme comprises a rearview mirror adjusting scheme and a seat adjusting scheme which are automatically given by the system and aim at the height and sitting height of the driver, and a schematic diagram of how to adjust the rearview mirror and the seat is given on a terminal screen.
The personalized content pushing comprises training records, training tracks, examination requirements of all items, learning videos, examination scores and the like of a driver.
The learning condition of the driver comprises training records, examination scores, error statistics of the driver and the like.
example 1:
As shown in fig. 2, the driver reads the physical card numbers of the IC card and the ID card through the driver ID card sensor for checking the identity of the coach driver, gives an initial tag according to the height and age information, and matches the initial teaching plan according to the initial tag;
the field driving camera dynamically captures the posture of a driver, and the eyes of the driver are fed back to remind the driver of the sitting posture and the eyes of the driver to be switched between the front rearview mirror and the left rearview mirror and the right rearview mirror; the vehicle state sensor acquires vehicle information through an on-board computer, such as: vehicle speed, vehicle light information: dipped headlight, high beam, left turn light, right turn light, safety belt and vehicle door state; according to the B1 driver image processing module, the B2 judging and deducting module uses a high-definition camera and an OBD sensor to judge whether other test rules such as vehicle line pressing are qualified or not and feeds back to remind a driver of driving; b3 the current training plan adjusting module dynamically adjusts the label of the current driver according to the action characteristics, learning characteristics and deduction points of the current driver, and then adjusts the current teaching plan, the cloud computing module is used for uploading the training records and training tracks of the driver to the cloud end to analyze the appropriate and efficient teaching plan and optimize the standard teaching plan, the driver can check the training records and the training tracks of the driver from the mobile phone end of the student, the examination of each project needs to be led, learning videos, examination scores and the like, the driver can also evaluate and feed back the problems from the vehicle and the field used by the mobile phone end of the student, a coach and the training records and the scores of the driver from the mobile phone end of the coach can check the examinations at each time, and check the wrong statistics of the driver and the like.
Example 2:
An example of a method for teaching a personalized driver training system is as follows:
1. When the driver registers, the C cloud computing module pre-distributes teaching plans according to the age and the school calendar. The backing and warehousing project comprises three teaching cases: the driver is pre-allocated to reverse 1. the characteristic is that the driver only makes one direction and the time is late. As long as the reaction of the driver is fast enough, the driver can be in place at one time, and the training is fast;
2. after a driver gets on the vehicle, the A2 driver shoots a camera to capture the characteristics of the face and the shoulders of the driver, gives a suggested rearview mirror adjustment scheme and a suggested seat adjustment scheme, and gives a schematic diagram on a terminal screen;
3. the driver begins to train to back a car and enter a warehouse, the user terminal module gives a prompt according to a teaching plan of 'reverse 1', 'please buckle a safety belt', 'please hang a reverse gear', 'please control the speed of the car', 'please fill the direction left immediately', and when the driver does not act in place, a correction prompt 'the speed of the car is too fast' and 'the direction is not filled in time' is given in time;
4. The method is characterized in that the direction is played in two sections, the first one is played for one circle, and the teaching plan is played after two seconds, so that the method is favorable for the driver with slow response;
5. The driver continues training according to the reverse 2 teaching plan during training, but the speed of the vehicle is still too fast for the driver;
The D user terminal module decides to call a coach for manual instruction, and the D2 coach mobile phone end of the coach B receives a prompt to instruct a driver how to reasonably control the speed;
7. after the training is finished, the D1 student mobile phone end of the driver receives track and error prompts of the training in the same day, and the self-duplication improvement is realized.
example 3:
further illustrating a method for teaching a personalized driver training system:
s01, a driver fills out identity data, a student camera is shot to the A2 after getting on the bus to collect a picture, the C cloud computing module gives a seat and rearview mirror adjusting suggestion according to the filled-in data and picture analysis, and the C cloud computing module automatically distributes a label 1 to the driver, such as high, short-sighted and good in movement; according to the label 1, the C cloud computing module automatically selects a teaching plan, such as 'no correction in place';
S02, a driver learns the first project and carries out primary training under the guidance of the D user terminal module to generate a deduction item; a2 shooting a student camera to capture bad actions of a driver during training in the whole process, such as the fact that the gaze directions of eyes are not right, a neck is extended, and a probe is detected; repeating the training to generate more deduction items; c1 cloud personalized teaching plan adjustment, namely adjusting the label of a driver to be label 2 according to the deduction item, and correcting the teaching plan by the system according to the label 2;
S03, after training is finished, the driver receives the learning record at the D1 student mobile phone end, the coach receives the learning record of the driver at the D2 coach mobile phone end, and the driver and the coach can play back and comment; c1 cloud personalized teaching plan adjustment adjusting a driver label 'tag 3' according to feedback of a driver and a coach, and correcting the teaching plan according to the 'tag 3' by the system;
s04, adjusting and collecting excellent coach teaching methods by using the C3 cloud-side reference teaching plan, summarizing the excellent coach teaching methods, generating a plurality of sets of reference teaching plans, evaluating the passing rate of the reference teaching plan according to the training condition and the examination condition of a driver, and adjusting and optimizing the reference teaching plan;
And S05, in the S04 process, the C cloud computing module collects data of a driver, a coach and a training field all the time, calculates the data and adjusts a reference teaching plan.
the invention can provide different teaching plans, dynamically collects the driving action and the posture (the strength of eyes, hands, shoulders and brake accelerator) of the driver through the A2 driver shooting camera, and matches the teaching plan of the proper driver.
the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. A personalized driver training system, characterized by: the system comprises a sensor module A, an edge calculation module B, a cloud calculation module C and a user terminal module D:
the sensor module A comprises a driver identity authentication sensor A1, a camera A2, a vehicle state sensor A3 and a judgment deduction sensor A4; the A1 driver identity authentication sensor carries out identity verification on a driver, allocates an initial label containing the height and the age of the driver, and generates a matched initial teaching plan through the initial label; the A2 driver takes photos at regular time when shooting the camera for training, and the behavior of the driver is analyzed; the A3 vehicle state sensor acquires vehicle information through a vehicle-mounted computer; the A4 judgment deduction sensor records the incorrect action of the driver in the training process;
the B edge calculation module comprises a B1 driver image processing module, a B2 judgment deduction module and a B3 current training plan adjusting module; the B1 driver image processing module collects the front data of the driver, the camera is used for dynamically capturing the eye position, hand position and shoulder position posture of the driver in the training process, a suggested adjusting scheme is given through voice, and unreasonable attention points are reminded in time; the B2 judgment and deduction module detects and receives the record of the A4 judgment and deduction sensor and then judges a deduction action; the B3 current training plan adjusting module dynamically adjusts the label of the current driver according to the action characteristics, the learning characteristics and the deduction points of the current driver, and further adjusts the current teaching plan;
The C cloud computing module comprises C1 cloud personalized teaching plan adjustment, C2 cloud vehicle and site grading adjustment and C3 cloud benchmark teaching plan adjustment; the C1 cloud personalized teaching plan is adjusted to obtain a proper and efficient training teaching plan through statistics and analysis of behavior habits of a driver during training in the cloud, and error operation and habits of the driver are gradually guided and corrected; the grading adjustment of the C2 cloud vehicle and the site enables a driver to evaluate and feed back the vehicle and the site used in training through the vehicle-mounted terminal and the D1 student mobile phone end, and a worker confirms and corrects the problem; c3 cloud benchmark teaching plan adjustment is carried out to collect excellent coach teaching methods and summarize the excellent coach teaching methods, a plurality of sets of benchmark teaching plans are generated, the passing rate of the benchmark teaching plans is evaluated according to the training condition and the examination condition of a driver, and the optimized benchmark teaching plans are adjusted;
The D user terminal module comprises a D1 student mobile phone end and a D2 coach mobile phone end; the D1 student mobile phone end can receive personalized content push of the C cloud computing module; and the D2 coach mobile phone end can receive the pushing of the learning condition of the driver of the C cloud computing module.
2. The system of claim 1, wherein: a2 shoots the student's camera and is wide-angle high definition infrared camera, can carry out the analysis to the picture scene of shooing, finds the position of eye, hand, shoulder to watch the angle to the eye and carry out the analysis, C high in the clouds calculation module is according to these information, distributes initial label.
3. the system of claim 1, wherein: in the driver training process, the B edge computing module collects the attention distribution condition, the hand-eye coordination condition and the vehicle learning score of a driver, deducts points and reports the points to the C cloud computing module, and after the C cloud computing module calculates the points, the label of the driver is correspondingly adjusted.
4. the system of claim 1, wherein: a3 vehicle state sensor includes the OBD sensor, acquires vehicle information through on-vehicle computer, if: vehicle speed, vehicle light information: dipped headlight, high beam, left turn signal, right turn signal, safety belt and door state.
5. the system of claim 1, wherein: the A4 judging and deducting sensor consists of an OBD sensor, a high-definition camera and a GPS; and the B2 judging and deducting module judges whether other test rules such as whether the vehicle is pressed are qualified or not by using a high-definition camera and an OBD sensor.
6. the system of claim 1, wherein: the adjusting scheme comprises a rearview mirror adjusting scheme and a seat adjusting scheme which are automatically given by the system and aim at the height and sitting height of the driver, and a schematic diagram of how to adjust the rearview mirror and the seat is given on a terminal screen.
7. the system of claim 1, wherein: the personalized content pushing comprises training records, training tracks, examination requirements of all items, learning videos, examination scores and the like of a driver.
8. the system of claim 1, wherein: the learning condition of the driver comprises training records, examination scores, training error statistics of the driver and the like.
9. a teaching method of a personalized driver training system is characterized in that: the personalized driver training system of any one of claims 1 to 8 is adopted, a driver is assigned with a label by the C cloud computing module from beginning to end, the C cloud computing module organizes teaching contents according to the label of the driver, the label is from basic information of the driver, training data collected by the A sensor module and the B edge computing module, a coach and driver feedback information collected by the D user terminal module, and the teaching contents combined with the label are personalized teaching plans, including teaching contents and teaching methods, such as a route for backing up, prompting contents and a prompting mode, and the teaching method of the personalized driver training system comprises the following steps:
S01, a driver fills out identity data, a student camera is shot to the A2 after getting on the bus to collect a picture, the C cloud computing module gives a seat and rearview mirror adjusting suggestion according to the filled-in data and picture analysis, and the C cloud computing module automatically distributes a label 1 to the driver, such as high, short-sighted and good in movement; according to the label 1, the C cloud computing module automatically selects a teaching plan, such as 'no correction in place';
S02, a driver learns the first project and carries out primary training under the guidance of the D user terminal module to generate a deduction item; a2 shooting a student camera to capture bad actions of a driver during training in the whole process, such as the fact that the gaze directions of eyes are not right, a neck is extended, and a probe is detected; repeating the training to generate more deduction items; c1 cloud personalized teaching plan adjustment, namely adjusting the label of a driver to be label 2 according to the deduction item, and correcting the teaching plan by the system according to the label 2;
S03, after training is finished, the driver receives the learning record at the D1 student mobile phone end, the coach receives the learning record of the driver at the D2 coach mobile phone end, and the driver and the coach can play back and comment; c1 cloud personalized teaching plan adjustment adjusting a driver label 'tag 3' according to feedback of a driver and a coach, and correcting the teaching plan according to the 'tag 3' by the system;
S04, adjusting and collecting excellent coach teaching methods by using the C3 cloud-side reference teaching plan, summarizing the excellent coach teaching methods, generating a plurality of sets of reference teaching plans, evaluating the passing rate of the reference teaching plan according to the training condition and the examination condition of a driver, and adjusting and optimizing the reference teaching plan;
And S05, in the S04 process, the C cloud computing module collects data of a driver, a coach and a training field all the time, calculates the data and adjusts a reference teaching plan.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112116508A (en) * | 2020-09-30 | 2020-12-22 | 易显智能科技有限责任公司 | Method and related device for judging driving training weak items |
| CN112644399A (en) * | 2020-12-29 | 2021-04-13 | 上海思快奇教育科技有限公司 | Education resource platform system based on internet |
| CN113223356A (en) * | 2021-05-13 | 2021-08-06 | 深圳市技成科技有限公司 | Skill training and checking system for PLC control technology |
| CN114419950A (en) * | 2022-01-22 | 2022-04-29 | 易显智能科技有限责任公司 | Big data analysis-based driving training teaching optimization method and system |
| CN116167900A (en) * | 2023-02-22 | 2023-05-26 | 广州极智信息技术有限公司 | Score display analysis system and method based on artificial intelligence |
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2019
- 2019-10-11 CN CN201910961977.2A patent/CN110544409A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112116508A (en) * | 2020-09-30 | 2020-12-22 | 易显智能科技有限责任公司 | Method and related device for judging driving training weak items |
| CN112644399A (en) * | 2020-12-29 | 2021-04-13 | 上海思快奇教育科技有限公司 | Education resource platform system based on internet |
| CN113223356A (en) * | 2021-05-13 | 2021-08-06 | 深圳市技成科技有限公司 | Skill training and checking system for PLC control technology |
| CN114419950A (en) * | 2022-01-22 | 2022-04-29 | 易显智能科技有限责任公司 | Big data analysis-based driving training teaching optimization method and system |
| CN116167900A (en) * | 2023-02-22 | 2023-05-26 | 广州极智信息技术有限公司 | Score display analysis system and method based on artificial intelligence |
| CN116167900B (en) * | 2023-02-22 | 2023-09-12 | 广州极智信息技术有限公司 | Score display analysis system and method based on artificial intelligence |
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