CN110060538A - Personalized artificial based on historical data modeling intelligently drives training and practices system and method - Google Patents
Personalized artificial based on historical data modeling intelligently drives training and practices system and method Download PDFInfo
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- CN110060538A CN110060538A CN201910285434.3A CN201910285434A CN110060538A CN 110060538 A CN110060538 A CN 110060538A CN 201910285434 A CN201910285434 A CN 201910285434A CN 110060538 A CN110060538 A CN 110060538A
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Abstract
Training is intelligently driven the present invention provides a kind of personalized artificial based on historical data modeling and practices system and method, by the data interaction with external system, acquires training information;Student's basic information in training information is obtained, characteristic model modeling is carried out to student's basic information, trainee is treated using characteristic model and carries out base categories;Student Training's information in training information is obtained, capability model modeling is carried out to Student Training's information, Utilization ability model treats trainee and carries out driving ability classification;Based on to base categories classification, driving ability class categories belonging to trainee, recommend individualized training teaching notes.Using data interaction and modeling of class, personalized training teaching notes are provided for trainee, and personalized training of driving is implemented by car-mounted terminal and is practiced, and are conducive to the deficiency for making up existing driver training robot.
Description
Technical field
Training is driven the present invention relates to intelligence and practices field, and in particular, to a kind of personalized artificial intelligence based on historical data modeling
Training can be driven and practice system and method.
Background technique
Since 2000, private savings automobile Chinese big small city obtained it is quick popularize, per year over 20000000 ginsengs
The driving for the problem of on Driving study, and outstanding Driving Instructor person and qualified teaching resource are obviously insufficient, bring
The cost of study increases year by year, and First Pass Yield is not high, and learning cycle is long.With the development of information technology, training technology is intelligently driven
It is gradually commercialized, by increasing sensing system on training vehicle, in conjunction with back-end data analysis system, realizes in real time certainly
Dynamic driving behavior feedback, helps student to improve driving ability in the environment of no coach.Although intelligence is driven training and is had already appeared
For many years, but mostly it is all based on fixed teaching content and teaching method, the historical record for not making full use of student to drive has
The short slab and shortcoming for targetedly improving student, realize individualized teaching.
The prior art relevant to the application is patent document CN107622318A, discloses a kind of reserve for Driving Test student and trains
The operation platform and method for running of instruction, including student's mobile terminal APP, coach mobile terminal APP, vehicle driving simulator, backstage pipe
Reason system, map server, student's mobile terminal APP, coach mobile terminal APP and background management system between by wireless network into
The transmission of row data, is carried out data transmission between vehicle driving simulator, map server and background management system by network.Student moves
Dynamic terminal APP can reserve drive simulating practice course, outdoor real vehicle practice course, can also consult drive simulating practice course
Present in mistake and interpretation of result;Coach mobile terminal APP can reserve outdoor real vehicle practice course;Vehicle driving simulator car body
It is reequiped using true vehicle or the mechanical equipment with true vehicle appearance, Combining with technology of virtual reality restores true traffic environment, and is real
Shi Jilu drive simulating practices procedural information;The internet that system manager is trained by background management system realization driving school+
Artificial intelligence management effect.
Summary of the invention
For the defects in the prior art, the object of the present invention is to provide a kind of personalized artificial intelligence based on historical data modeling
Training can be driven and practice system and method.
A kind of personalized artificial intelligence driving training system based on historical data modeling provided according to the present invention, comprising:
System interface module: by the data interaction with external system, the training information for the student that graduated is acquired;
Student's characteristics analysis module: obtaining student's basic information in training information, carries out characteristic model to Student Training's information
Modeling treats trainee using characteristic model and carries out base categories;
Driving behavior analysis module: obtaining Student Training's information in training information, carries out capability model to student's basic information
Modeling, Utilization ability model treat trainee and carry out driving ability classification;
Intelligent teaching notes recommend decision-making module: based on to base categories classification, driving ability class categories belonging to trainee, pushing away
Recommend individualized training teaching notes.
Preferably, the personalized artificial intelligence driving training system based on historical data modeling, further includes:
Student's driving data service module: by interconnecting with car-mounted terminal, being issued to car-mounted terminal for individualized training teaching notes, real
When receive feedback data of individualized training teaching notes during driving training;
Car-mounted terminal module: receiving individualized training teaching notes, carries out driving training according to individualized training teaching notes, uploads and drive training
Driving behavior data during instruction.
Preferably, the personalized artificial intelligence driving training system based on historical data modeling, further includes:
Training organization's resource management module: resource allocation is carried out to training organization, the training organization is that driving training process mentions
For training resource;
Student Training's management module: the driving training process for treating trainee provides information management, and the information management includes
Any one of class hour arrangement, appointment scheduling, student's authentication, training time arrangement are appointed a variety of;
Information pushes management module: collecting to Student Training's information during the driving training of trainee, is by outside
System carries out information push.
A kind of personalized artificial based on historical data modeling provided according to the present invention intelligently drives culture method, comprising:
System interface step: by the data interaction with externalist methodology, training information is acquired;
Student's signature analysis step: student's basic information in analysis training information carries out character modules according to history training record
Type modeling treats trainee using characteristic model and carries out base categories;
Driving behavior analysis step: Student Training's information in analysis training information carries out ability mould according to history training record
Type modeling, Utilization ability model treat trainee and carry out driving ability classification;
Intelligent teaching notes recommend steps in decision-making: based on to base categories classification, driving ability class categories belonging to trainee, pushing away
Recommend individualized training teaching notes.
Preferably, the personalized artificial based on historical data modeling intelligently drives culture method, further includes:
Student's driving data service steps: by interconnecting with car-mounted terminal, being issued to car-mounted terminal for individualized training teaching notes, real
When receive feedback data of individualized training teaching notes during driving training;
Car-mounted terminal step: receiving individualized training teaching notes, carries out driving training according to individualized training teaching notes, uploads and drive training
Driving behavior data during instruction.
Preferably, the personalized artificial based on historical data modeling intelligently drives culture method, further includes:
Training organization's resource management step: resource allocation is carried out to training organization, the training organization is that driving training process mentions
For training resource;
Student Training's management process: the driving training process for treating trainee provides information management, and the information management includes
Any one of class hour arrangement, appointment scheduling, student's authentication, training time arrangement are appointed a variety of;
Information pushes management process: collecting to Student Training's information during the driving training of trainee, by outside side
Method carries out information push.
Preferably, the external system include wechat public platform, business APP, driving school's management system, in artificial intelligence driving training system
It is any or appoint it is a variety of;
Student's basic information includes student's gender, student's age, student's blood group, student's educational background, institute's learning type, overall merit
Any one of information is appointed a variety of;
Student Training's information include project training class hour number, project training number, project qualification rate, project single complete when
Between, rehearsal project qualification rate, training interval time, project make a mistake and any one of number or appoint a variety of.
Preferably, the driving training is that car-mounted terminal broadcasts individualized training teaching notes progress voice broadcast, picture presentation, video
It puts, any one of text importing or appoint a variety of.
Preferably, the training resource includes coach's car state, mobile unit, the coach's state, training court of training organization
Any one of or appoint it is a variety of.
Preferably, the base categories, driving ability classification use static analysis.
Compared with prior art, the present invention have it is following the utility model has the advantages that
1, the present invention uses data interaction and modeling of class, personalized training teaching notes is provided for trainee, and pass through vehicle
Mounted terminal is implemented personalized training of driving and is practiced, and the deficiency for making up existing driver training robot is conducive to;
2, the present invention driver behavior model mature by building, and effective data characteristics set, by a large amount of
Complicated history driving training record data realize the validity feature collection of driving behavior by varigrained segmentation and arrangement
It closes, and feeds back into the driving behavior model of student, finally make personalized training program.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention, purpose
It will be become more apparent upon with advantage:
Fig. 1 is system framework schematic diagram of the invention;
Fig. 2 is method implementation flow chart of the invention;
Fig. 3 is student's tagsort schematic diagram;
Fig. 4 is driving behavior data classification schematic diagram.
Specific embodiment
The present invention is described in detail combined with specific embodiments below.Following embodiment will be helpful to those skilled in the art
The present invention is further understood, but the invention is not limited in any way.It should be pointed out that those skilled in the art
For, without departing from the inventive concept of the premise, several changes and improvements can also be made.These belong to guarantor of the invention
Protect range.
The present invention relates to artificial intelligence driving training systems, especially for different abilities, the motor vehicle driving trainer of all ages and classes
Member is formulated it personalized teaching and training plan, is reached and improved learning efficiency by the training effect based on its history, is advised
Model teaching method realizes personalized teaching notes intelligent training student.By driver's behavior model that building is mature, and effectively
Data characteristics set, data are recorded by history driving training to large amount of complex, by varigrained segmentation and arrangement,
It realizes the validity feature set of driving behavior, and feeds back into the driving behavior model of student, finally make personalized training
Instruction plan.For the deficiency of existing driver training robot coaching system, one kind is provided for student using personalized religion
The artificial intelligence driving training system of case (teaching programme+teaching method), this method effectively can drive training using the history of student
The effect for instructing data and student's driver training makes the personalized teaching programme and teaching method for all kinds of students.
A kind of personalized artificial intelligence driving training system based on historical data modeling provided according to the present invention, comprising:
System interface module: by the data interaction with external system, the training information for the student that graduated is acquired;
Student's characteristics analysis module: student's basic information in analysis training information carries out characteristic model to student's basic information
Modeling treats trainee using characteristic model and carries out base categories;
Driving behavior analysis module: Student Training's information in analysis training information carries out capability model to Student Training's information
Modeling, Utilization ability model treat trainee and carry out driving ability classification;
Intelligent teaching notes recommend decision-making module: based on to base categories classification, driving ability class categories belonging to trainee, pushing away
Recommend individualized training teaching notes.Wherein, student's basic information uses statistical data analysis, history training record application dynamic data point
Analysis carries out analysis respectively and sorts out modeling, and using model according in the feature for learning student, basic classification is carried out to student.
Specifically, the personalized artificial intelligence driving training system based on historical data modeling, further includes:
Student's driving data service module: by interconnecting with car-mounted terminal, being issued to car-mounted terminal for individualized training teaching notes, real
When receive feedback data of individualized training teaching notes during driving training;
Car-mounted terminal module: receiving individualized training teaching notes, carries out driving training according to individualized training teaching notes, uploads and drive training
Driving behavior data during instruction.Preferably, it realizes and is interconnected with various high accuracy positioning car-mounted terminals, realize personalized teaching notes
Transmission, analysis, feedback with real-time driving behavior feedback data.
Specifically, the personalized artificial intelligence driving training system based on historical data modeling, further includes:
Training organization's resource management module: resource allocation is carried out to training organization, the training organization is that driving training process mentions
For training resource;
Student Training's management module: the driving training process for treating trainee provides information management, and the information management includes
Any one of class hour arrangement, appointment scheduling, student's authentication, training time arrangement are appointed a variety of;
Information pushes management module: collecting to Student Training's information during the driving training of trainee, is by outside
System carries out information push.
A kind of personalized artificial based on historical data modeling provided according to the present invention intelligently drives culture method, comprising:
System interface step: by the data interaction with externalist methodology, training information is acquired;
Student's signature analysis step: student's basic information in analysis training information carries out character modules according to history training record
Type modeling treats trainee using characteristic model and carries out base categories;
Driving behavior analysis step: Student Training's information in analysis training information carries out ability mould according to history training record
Type modeling, Utilization ability model treat trainee and carry out driving ability classification;
Intelligent teaching notes recommend steps in decision-making: based on to base categories classification, driving ability class categories belonging to trainee, pushing away
Recommend individualized training teaching notes.Preferably, intelligent teaching notes recommend decision to realize that personalized teaching notes recommend decision, utilize student's characteristic
Accordingly and driving behavior data, the strategy process that training teaching notes are driven in personalization is formulated for student.Wherein, personalized teaching notes are by teaching
Learn plan and teaching method composition.Teaching programme is combined training content and the basic class hour array of training at different students are based on
Its feature is corresponding to recommend different teaching programmes;Teaching method is the examination request according to projects, formulates the teaching of projects
Event.
Specifically, the personalized artificial based on historical data modeling intelligently drives culture method, further includes:
Student's driving data service steps: by interconnecting with car-mounted terminal, being issued to car-mounted terminal for individualized training teaching notes, real
When receive feedback data of individualized training teaching notes during driving training;
Car-mounted terminal step: receiving individualized training teaching notes, carries out driving training according to individualized training teaching notes, uploads and drive training
Driving behavior data during instruction.
Specifically, the personalized artificial based on historical data modeling intelligently drives culture method, further includes:
Training organization's resource management step: resource allocation is carried out to training organization, the training organization is that driving training process mentions
For training resource;
Student Training's management process: the driving training process for treating trainee provides information management, and the information management includes
Any one of class hour arrangement, appointment scheduling, student's authentication, training time arrangement are appointed a variety of;
Information pushes management process: collecting to Student Training's information during the driving training of trainee, by outside side
Method carries out information push.
Specifically, the external system include wechat public platform, business APP, driving school's management system, in artificial intelligence driving training system
It is any or appoint it is a variety of;Preferably, by driving training rudimentary knowledge, training court information, the basic knowledge of projects training
And its information such as training skills, individualized training teaching notes, training effect, it is targetedly pushed to using wechat public platform, APP
Student, so that student previews, reviews.
Student's basic information includes student's gender, student's age, student's blood group, student's educational background, institute's learning type, overall merit
Any one of information is appointed a variety of;
Student Training's information include project training class hour number, project training number, project qualification rate, project single complete when
Between, rehearsal project qualification rate, training interval time, project make a mistake and any one of number or appoint a variety of.Preferably, by training machine
Training, the student certification of identity, the collection of training time composition on learner-driven vehicle are reserved in the arrangement of structure class hour, student.
Specifically, the driving training is that car-mounted terminal broadcasts individualized training teaching notes progress voice broadcast, picture presentation, video
It puts, any one of text importing or appoint a variety of.
Specifically, the training resource includes coach's car state, mobile unit, the coach's state, training court of training organization
Any one of or appoint it is a variety of.Preferably, pipe is carried out to the learner-driven vehicle of training organization, mobile unit, coach, training court etc.
Reason, the planning arrangement of whether enabling disabling, coach's working of vehicle, vehicle, training court that coach uses etc..
Specifically, the base categories, driving ability classification use static analysis.Preferably, student's signature analysis realizes student
Static nature analysis method is split description to student's static data, in conjunction with the assessment method of student's integration capability, from understanding
Ability, memory capability, direction distance perception, the reaction coordination ability, attitude towards study, quality, security civilization drive the side such as consciousness at heart
Face, which is pressed, scores to student, then obtains integration capability assessment result to the end according to average weighted method.Student's driving behavior
It realizes student's driving behavior analysis method, by carrying out feature extraction to student's driving behavior data, forms the driving row of student
Be characterized, then according to the different behaviors showed in each test subject, to behavior expression of the student on each single item subject into
Row evaluation, and the finally comprehensive Comprehensive Assessment for obtaining student's driving ability.Working as student can be obtained by driving behavior analysis
The evaluation of preceding driving ability, compare history driving ability evaluation can analyze out student to history drive training teaching notes (drive training content
With drive culture method) adaptability.
Personalized artificial provided by the invention based on historical data modeling intelligently drives training training system, can be by being based on history
The step process that the personalized artificial of data modeling intelligently drives training training method is realized.Those skilled in the art can will be based on going through
The personalized artificial of history data modeling intelligently drives training training method and is interpreted as the personalized artificial based on historical data modeling
Intelligence drives the preference of training training system.The present invention is primarily based on student's feature and formulates personalized basic teaching notes, then basis
The driving behavior ability that student is constantly promoted during actual learning constantly corrects student's behavioral characteristics, and to current teaching notes
Learning effect fed back, gradually recommend the personalized teaching notes for more meeting current student's ability.Simultaneously during Student Training,
The current personalized teaching notes of student can be adjusted in due course based on the practical driving behavior ability of student currently.
As shown in Figure 1, the present invention includes system interface, student's characteristic analysis system, student's driving behavior analysis system, intelligence religion
Case recommends decision system, student's driving data service system, training organization's resource management module, Student Training's management module, letter
9 parts such as breath push management module, system database.The present invention is the motor vehicle driving training for different abilities, all ages and classes
Instruction personnel formulate it personalized teaching and training plan by the training effect based on its history, reach raising study effect
Rate, canonical teaching method realize personalized teaching notes intelligent training student.One kind is provided for student using personalized teaching notes (religion
Plan+teaching method) artificial intelligence driving training system, can effectively utilize student history driving training data and
The effect of member's driver training makes the personalized teaching programme and teaching method for all kinds of students.
Wherein, system interface be in student's characteristic, reservation training data, the relevant information of training, training effect data etc.,
By system interface in wechat public platform, related service APP (such as student's questionnaire, driving training knowledge learning, integration capability assessment
Deng application), mutually transmit between driving school's management system and artificial intelligence driving training system.Student's characteristic analysis system is by from wechat
The gender for the student that public platform, driving school's management system and other APP are obtained, age, blood group, educational background, institute's learning type, comprehensive energy
The information such as power composition carries out analysis by the history training record for the student that graduated and sorts out modeling, is learned using model according in
The feature of member will carry out basic classification in student.Student's driving behavior analysis system be by student in training process
Generation training class hour number, projects training class hour number, projects frequency of training, projects effective degree, projects qualification rate,
Projects single deadline, rehearsal project qualification rate, training interval time, projects make a mistake the composition such as number, by having finished
The history training record of industry student is analyzed, and student's driving ability model is established, using model according to learn student in training
In driving behavior data, will learn student carry out driving ability classification.It is to pass through driving school that intelligent teaching notes, which recommend decision system,
Management system obtains the history training record of student of having graduated and analyzes, in conjunction with student's characteristic model and driving ability model,
Recommend decision making algorithm by teaching notes, establish the teaching notes model of all kinds of students, using teaching notes model and student characteristic type and its
Driving ability type is learning the personalized training teaching notes of student's recommendation to be all kinds of.Teaching notes are by the content of courses (plan) and teaching side
How method composition, i.e. what project of every class training, projects train, and personalized teaching notes include basic teaching notes and optimization teaching notes
(adjusting teaching content and teaching method in due course).Student's driving data service system is to realize and various high accuracy positionings vehicle-mounted end
Transmission, the analysis, feedback of personalized teaching notes and real-time driving behavior feedback data are realized in end interconnection.It will learn student's first
Personalized teaching notes are issued to high accuracy positioning car-mounted terminal, are trained for corresponding student;Then from " high accuracy positioning is vehicle-mounted
The driving behavior data that terminal " acquisition student generates in training are analyzed, and are fitted accordingly to the personalized teaching notes of student
When adjust and feedback.
Training organization's resource management module: to the learner-driven vehicle of training organization, mobile unit, coach, training court etc. into
Row management, the planning arrangement of whether enabling disabling, coach's working of vehicle, vehicle, training court that coach uses etc..It learns
Member's training management module is to reserve training, the certification of student's identity on learner-driven vehicle, instruction by the arrangement of training organization's class hour, student
Practice the composition such as collection of time.Information push management module is by driving training rudimentary knowledge, training court information, projects instruction
The information such as experienced basic knowledge and its training skills, individualized training teaching notes, training effect, have needle using wechat public platform, APP
Student is pushed to property, so that student previews, reviews.High accuracy positioning car-mounted terminal is passed through according to the teaching notes received
The modes such as voice broadcast, picture presentation, video playing, text importing instruct Student Training;It will be in the driving behavior data of student
It is transmitted to artificial intelligence driving training system.
As shown in Fig. 2, the application process of entire artificial intelligence driving training system is as follows in the implementation of specific driving training:
Step 1: derived from " driving school's management system " " graduation student training records data " being imported into artificial intelligence and drives training system
System;History training record data are disposably imported when initialization, later by with the training record of every 500 students be a list
Position imports, the same processing of student training record that this system generates;
Step 2: " student's characteristic analysis system " carries out analysis classification to " graduation student training records data ", establishes student's feature
Model simultaneously saves, and the postgraduate training record data of student Jing Guo this system training is recycled constantly to correct student's feature
Model simultaneously saves;
Step 3: " driving behavior analysis system " analyzes " graduation student training records data ", establishes student's driving ability
Model simultaneously saves, and the postgraduate training record data of student Jing Guo this system training is recycled constantly to correct the model and protect
It deposits;
Step 4: " teaching notes recommendation decision system " analyzes " graduation student training records data ", in conjunction with student's characteristic model
With driving ability model, recommends decision making algorithm by teaching notes, establish student's individualized basis teaching notes model and save, recycle warp
The postgraduate training record data of student for crossing this system training, which are constantly corrected, recommends decision making algorithm and individualized basis teaching notes
Model simultaneously saves;
Step 5: learning student's characteristic typing intelligence driving training system, passing through student's character modules of " student's characteristic analysis system "
Type and " teaching notes recommendation decision system " teaching notes model formulate individualized basis teaching notes for the student;After characteristic typing immediately
It calculates, instant computing corrects individualized basis teaching notes after supplement or modification characteristic;
Step 6: after student reserves training, information pushes management module and the individualized basis teaching notes of the student is pushed to
Member, student carry out previewing teaching notes using wechat public platform and APP, reserve successfully push immediately;
Step 7: student needs to carry out authentication in the car-mounted terminal on learner-driven vehicle used, car-mounted terminal will before real vehicle training
Student's identity information is sent to artificial intelligence driving training system in real time, and the individualized basis teaching notes of the student are sent to by system in real time
Corresponding learner-driven vehicle car-mounted terminal;
Step 8: student starts real vehicle training, and car-mounted terminal instructs the Student Training according to individualized basis teaching notes, generates in real time
Driving behavior data simultaneously upload artificial intelligence driving training system in real time;
Step 9: driving behavior analysis system handles the driving behavior data of the student once received per minute, and adjustment should in due course
The driving ability of student, teaching notes recommend decision system to adjust teaching notes in due course accordingly, teaching notes real time down car-mounted terminal adjusted,
Car-mounted terminal instructs Student Training according to the new teaching notes received;
Step 10: after Student Training, driving behavior analysis system is directed to immediately at the driving behavior data of the student
Reason, adjusts the driving ability of the student in time, and teaching notes recommendation decision system formulates the default teaching notes of a student lower class hour accordingly
And it saves;
Step 11: amendment of the training interval duration to default teaching notes, the amendment are handled immediately after the completion of reserving training.Between training
Refer to last training to this training interval time, in terms of week every duration.Small to be equal to 2 weeks, default teaching notes do not adjust;2
All < training interval duration≤4 week, driving ability lower 1 grade, and appropriate adjustment defaults teaching notes;4 weeks < training intervals duration≤8
Week, driving ability lower 2 grades, and appropriate adjustment defaults teaching notes;8 weeks < training intervals duration≤12 week, driving ability lower 3 grades,
Appropriate adjustment defaults teaching notes;12 weeks < training interval durations, driving ability are adjusted downward to initial level, and default teaching notes are adjusted to basis
Teaching notes.
As shown in figure 3, student's signature analysis is the characterization and quantification for realizing information of trainee, final building is based on student's feature
Model provides basic student's static nature data for intelligence driving training system.Information of trainee include it is varied, such as gender, year
The objective static informations such as age, blood group, educational background, institute's learning type, further include additionally obtained by questionnaire, investigation etc. it is comprehensive to student
The assessment of data of conjunction ability.The present invention forms effective student's feature by the analysis based on a large amount of history student data
Quantitative method is made of multiple subalgorithms, comprising:
Algorithm 1.1: student's static nature analysis method is divided student's static data and is described, and male and female are in age bracket
Segmentation on difference.
Algorithm 1.2: the assessment method of student's integration capability, coach assess each student by APP or wechat public platform, from
Understandability, memory capability, direction distance perception, the reaction coordination ability, attitude towards study, quality, security civilization drive consciousness at heart
Etc. by 1-5 to student score (1 point of minimum, 5 points of highest), then obtain synthesis energy to the end according to average weighted method
Force estimation is as a result, between specific shown in table one.
One student's integration capability evaluation example table of table
As shown in figure 4, driving behavior analysis carries out characterization extraction to student's driving behavior data, the driving behavior of student is formed
Feature carries out behavior expression of the student on each single item subject then according to the different behaviors showed in each test subject
Evaluation, and the finally comprehensive Comprehensive Assessment for obtaining student's driving ability.The current of student can be obtained by driving behavior analysis
The evaluation of driving ability, compare history driving ability evaluation can analyze out student to history drive training teaching notes (drive training content and
Drive culture method) adaptability.Driving behavior method is made of multiple subalgorithms, comprising: driving behavior data segmentation algorithm, it is right
In driving behavior data training class hour number, project rehearsal qualification rate, training interval time, projects training data determined
Driving behavior record data are become a quantitative digital feature by quantitatively evaluating.
Specific calculating is as follows:
1. driving behavior ability rating is temporarily divided into 5 grades, corresponding score value section is respectively corresponded, full marks are 1 point;
2. the calculation method of student's driving behavior ability in training process: score value is calculated according to following formula, by institute's score value
Matching obtains the driving behavior ability rating of student;
Driving behavior ability score value=training class hour score+rehearsal qualification rate+5 project scores of score
3. importance, the complexity of training program etc. of every score value specific gravity basic of distribution items determine, every subdivision in detail
It described in the following 4-6 item of scoring method, does not include " training interval time " item;
4. training class hour number is defined as the duration of student's real vehicle training, as unit of minute, stored count;Car-mounted terminal is to start
Training and the time difference for terminating training are to upload artificial intelligence driving training system immediately after training when time training duration, 6 points
The corresponding corresponding score value of section, full marks are 0.1 point;
5. project rehearsal qualification rate is defined as 5 projects, successively training is sentenced once as a rehearsal unit according to Examination Rate
It is fixed whether qualified, individually start training and terminate qualified rehearsal units in training closed loop divided by total rehearsal units multiplied by
100%, the corresponding corresponding score value of 4 segmentations, full marks are 0.15 point;
The complexity of 6.5 project practice study is different, and score value specific gravity and project difficulty are positively correlated, and 5 project total scores are
0.75 point, projects segment item score value specific gravity and score condition is as shown in Table 2, and projects subdivision item radix is to be trained according to history
The instruction record resulting average value of data;
Two projects of table segment item score value specific density table
Enter project warehouse compartment 7. project training class hour number is defined as learner-driven vehicle and leave the time difference of warehouse compartment, as unit of minute,
Carry out stored count;
Enter project warehouse compartment 8. project training number is defined as learner-driven vehicle and leave one closed loop of warehouse compartment as 1 project training,
Stored count;
9. project effective degree is defined as by the project project training number that completely requirement is completed, stored count;
10. project qualification rate be defined as individually starting training and terminate training closed loop in, the project training qualification number divided by
The project training total degree is multiplied by 100%;
Enter project warehouse compartment 11. the project single deadline is defined as learner-driven vehicle and leave the single closed loop spent time of warehouse compartment, with
Minute is unit;
Enter project warehouse compartment 12. errors number is defined as learner-driven vehicle and leave the errors number that the single closed loop of warehouse compartment is occurred, respectively
The requirement of project and difficulty difference, the erroneous point and quantity of definition are also different;
" 13. training interval time " amendment of the item to driving behavior ability, as shown in Table 3:
Three driving behavior capacity correction table of table
It is to be formulated based on student's characteristic and driving behavior data for student personalized that personalized teaching notes, which recommend decision,
Drive the strategy process of training teaching notes.What do personalized teaching notes (practice by teaching programme?) and teaching method (how to practice?) composition, teaching
Plan is divided into 4 parts, and the student at different sexes age, the order of 4 part teaching programmes and the basic class hour number of corresponding training are all
It is different;Teaching method is the examination request according to projects, formulates the teaching event of projects to realize.The teaching of 4 parts
The training content of plan are as follows: 1. ramp, curve, right angle, 2. reversing storage, 3. side parking, reversing storage, 4. project rehearsal,
Examination, examination are taught;3. 1. 4. and 1. 2. 3. 4. 2. 4 part teaching programmes training order is divided into, the basic class hour of each combination
Number is different according to the gender of student, age, and specific value is according to obtained by history training record data classification;Teaching programme is pressed
The method (method of order upgrading) that order is gradually implemented: the project in each combination training content meets score condition or satisfaction
When corresponding combined training basis class hour number, indicate that the combined training is completed, into next training combination;It is 4. combined when meeting the
When completion condition, it is proposed that two mock examination of student's subject, while reserving subject two and formally taking an examination;The implementation method of teaching method:
According to examination request, the driving trace that projects each have corresponding learner-driven vehicle driving process to generate, in this driving process,
It is teaching method by instructing student to do corresponding operation in certain positions;Driving trace point multiple stages of entire project are complete
At point several teaching events instruct student to do corresponding operation in each stage;It imparts knowledge to students by adjusting teaching event to adjust
Method, different regions, different driving schools can be different for student to meet respectively area, respective driving school with customized teaching event
Teaching method.Component content field has: stage number, stage description, Case Number, event description, trigger condition, remarks,
Voice prompting, text prompt, picture prompting.The upgrading of teaching method be projects teaching method by upgrading sequential arrangement be A,
B, C, D, E ..., the practice of each teaching method instructs student 3 times (one group) complete projects, according to the single item of every group of practice
Mesh average error number upgrades/degrades teaching method, without calculating when not completing sector.5 training programs according to
Complexity, every group of 3 sections of practice single project average error number point respectively correspond " rising level-one ", " holding ", " drop level-one " 3
A rule, the segmentation for the number that malfunctions is according to obtained by history training record data.
One skilled in the art will appreciate that in addition to realizing system provided by the invention, dress in a manner of pure computer readable program code
Set and its modules other than, completely can by by method and step carry out programming in logic come so that system provided by the invention,
Device and its modules are with logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and embedded microcontroller
Deng form realize identical program.So system provided by the invention, device and its modules are considered one kind
Hardware component, and the structure that the module for realizing various programs for including in it can also be considered as in hardware component;It can also
Will be considered as realizing the module of various functions either the software program of implementation method can be in hardware component again
Structure.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned specific
Embodiment, those skilled in the art can make a variety of changes or modify within the scope of the claims, this has no effect on this
The substantive content of invention.In the absence of conflict, the feature in embodiments herein and embodiment can arbitrarily mutual group
It closes.
Claims (10)
1. a kind of personalized artificial intelligence driving training system based on historical data modeling characterized by comprising
System interface module: by the data interaction with external system, the training information for the student that graduated is acquired;
Student's characteristics analysis module: obtaining student's basic information in training information, carries out characteristic model to student's basic information
Modeling treats trainee using characteristic model and carries out base categories;
Driving behavior analysis module: obtaining Student Training's information in training information, carries out capability model to Student Training's information
Modeling, Utilization ability model treat trainee and carry out driving ability classification;
Intelligent teaching notes recommend decision-making module: based on to base categories classification, driving ability class categories belonging to trainee, pushing away
Recommend individualized training teaching notes.
2. the personalized artificial intelligence driving training system according to claim 1 based on historical data modeling, which is characterized in that
Further include:
Student's driving data service module: by interconnecting with car-mounted terminal, being issued to car-mounted terminal for individualized training teaching notes, real
When receive feedback data of individualized training teaching notes during driving training;
Car-mounted terminal module: receiving individualized training teaching notes, carries out driving training according to individualized training teaching notes, uploads and drive training
Driving behavior data during instruction.
3. the personalized artificial intelligence driving training system according to claim 1 based on historical data modeling, which is characterized in that
Further include:
Training organization's resource management module: resource allocation is carried out to training organization, the training organization is that driving training process mentions
For training resource;
Student Training's management module: the driving training process for treating trainee provides information management, and the information management includes
Any one of class hour arrangement, appointment scheduling, student's authentication, training time arrangement are appointed a variety of;
Information pushes management module: collecting to Student Training's information during the driving training of trainee, is by outside
System carries out information push.
4. a kind of personalized artificial based on historical data modeling intelligently drives culture method characterized by comprising
System interface step: by the data interaction with externalist methodology, the training information for the student that graduated is acquired;
Student's signature analysis step: obtaining student's basic information in training information, carries out characteristic model to student's basic information
Modeling treats trainee using characteristic model and carries out base categories;
Driving behavior analysis step: obtaining Student Training's information in training information, carries out capability model to Student Training's information
Modeling, Utilization ability model treat trainee and carry out driving ability classification;
Intelligent teaching notes recommend steps in decision-making: based on to base categories classification, driving ability class categories belonging to trainee, pushing away
Recommend individualized training teaching notes.
5. the personalized artificial according to claim 4 based on historical data modeling intelligently drives culture method, which is characterized in that
Further include:
Student's driving data service steps: by interconnecting with car-mounted terminal, being issued to car-mounted terminal for individualized training teaching notes, real
When receive feedback data of individualized training teaching notes during driving training;
Car-mounted terminal step: receiving individualized training teaching notes, carries out driving training according to individualized training teaching notes, uploads and drive training
Driving behavior data during instruction.
6. the personalized artificial according to claim 4 based on historical data modeling intelligently drives culture method, which is characterized in that
Further include:
Training organization's resource management step: resource allocation is carried out to training organization, the training organization is that driving training process mentions
For training resource;
Student Training's management process: the driving training process for treating trainee provides information management, and the information management includes
Any one of class hour arrangement, appointment scheduling, student's authentication, training time arrangement are appointed a variety of;
Information pushes management process: collecting to Student Training's information during the driving training of trainee, by outside side
Method carries out information push.
7. the personalized artificial intelligence driving training system or claim according to claim 1 based on historical data modeling
Personalized artificial based on historical data modeling described in 4 intelligently drives culture method, which is characterized in that the external system includes micro-
Believe any one of public platform, business APP, driving school's management system, artificial intelligence driving training system or appoints a variety of;
Student's basic information includes student's gender, student's age, student's blood group, student's educational background, institute's learning type, overall merit
Any one of information is appointed a variety of;
Student Training's information include project training class hour number, project training number, project qualification rate, project single complete when
Between, rehearsal project qualification rate, training interval time, project make a mistake and any one of number or appoint a variety of.
8. the personalized artificial intelligence driving training system or claim according to claim 2 based on historical data modeling
Personalized artificial based on historical data modeling described in 5 intelligently drives culture method, which is characterized in that the driving training is vehicle-mounted
Terminal carries out any one of voice broadcast, picture presentation, video playing, text importing to individualized training teaching notes or appoints more
Kind.
9. the personalized artificial intelligence driving training system or claim according to claim 3 based on historical data modeling
Personalized artificial based on historical data modeling described in 6 intelligently drives culture method, which is characterized in that the training resource includes training
It instructs any one of coach's car state of mechanism, mobile unit, coach's state, training court or appoints a variety of.
10. the personalized artificial intelligence driving training system or right according to claim 1 based on historical data modeling is wanted
Personalized artificial based on historical data modeling described in asking 4 intelligently drives culture method, which is characterized in that the base categories are driven
Capability Categories are sailed using static analysis.
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Cited By (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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Citations (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101561972A (en) * | 2008-04-17 | 2009-10-21 | 北京宣爱智能模拟技术有限公司 | Method and system for interactive automobile driving case teaching |
CN101739855A (en) * | 2008-11-07 | 2010-06-16 | 北京宣爱智能模拟技术有限公司 | Automobilism network personalized teaching system and learning method thereof |
CN103886054A (en) * | 2014-03-13 | 2014-06-25 | 中国科学院自动化研究所 | Personalization recommendation system and method of network teaching resources |
CN103971558A (en) * | 2014-05-16 | 2014-08-06 | 无锡海鸿信息技术有限公司 | Intelligent robot system for motor vehicle driver training |
US20150056577A1 (en) * | 2005-02-11 | 2015-02-26 | Raydon Corporation | Vehicle crew training system |
CN104618514A (en) * | 2015-03-05 | 2015-05-13 | 成都闰世科技有限公司 | Data processing method and device |
CN105808637A (en) * | 2016-02-23 | 2016-07-27 | 平安科技(深圳)有限公司 | Personalized recommendation method and device |
CN105824979A (en) * | 2016-06-07 | 2016-08-03 | 中国联合网络通信集团有限公司 | Course recommendation method and system of same |
CN105844998A (en) * | 2016-04-25 | 2016-08-10 | 安徽嘻哈网络技术有限公司 | Electronic trainer teaching and testing system |
CN105868317A (en) * | 2016-03-25 | 2016-08-17 | 华中师范大学 | Digital education resource recommendation method and system |
CN106251724A (en) * | 2016-10-10 | 2016-12-21 | 北京高途智驾科技有限公司 | A kind of driver's intelligent tutoring robot system |
CN106528812A (en) * | 2016-08-05 | 2017-03-22 | 浙江工业大学 | USDR model based cloud recommendation method |
CN106528656A (en) * | 2016-10-20 | 2017-03-22 | 杨瀛 | Student history and real-time learning state parameter-based course recommendation realization method and system |
CN106548238A (en) * | 2016-02-24 | 2017-03-29 | 浙江你若信息科技有限公司 | One kind drives training reserving method and system |
CN106651702A (en) * | 2016-12-30 | 2017-05-10 | 安徽宝信信息科技有限公司 | Intelligent voice driving training system |
CN106779867A (en) * | 2016-12-30 | 2017-05-31 | 中国民航信息网络股份有限公司 | Support vector regression based on context-aware recommends method and system |
CN106781876A (en) * | 2017-01-13 | 2017-05-31 | 南京多伦科技股份有限公司 | A kind of multifunctional intellectual vehicle-learning system |
CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
CN108510844A (en) * | 2018-04-16 | 2018-09-07 | 中国石油大学(华东) | A kind of full automatic vehicle driving instruction system |
US20180277011A1 (en) * | 2011-10-31 | 2018-09-27 | Lifelong Driver Llc | Driver training |
US10115173B1 (en) * | 2014-09-23 | 2018-10-30 | Allstate Insurance Company | System and method for standardized evaluation of driver's license eligibility |
CN108805308A (en) * | 2018-05-25 | 2018-11-13 | 王明文 | A kind of driving training APP practices reservation platform management system |
CN109492102A (en) * | 2018-11-08 | 2019-03-19 | 中国联合网络通信集团有限公司 | User data processing method, device, equipment and readable storage medium storing program for executing |
CN109543841A (en) * | 2018-11-09 | 2019-03-29 | 北京泊远网络科技有限公司 | Deep learning method, apparatus, electronic equipment and computer-readable medium |
CN109543963A (en) * | 2018-11-06 | 2019-03-29 | 深圳信息职业技术学院 | A kind of big data analysis method and system based on student's study habit |
-
2019
- 2019-04-08 CN CN201910285434.3A patent/CN110060538A/en active Pending
Patent Citations (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150056577A1 (en) * | 2005-02-11 | 2015-02-26 | Raydon Corporation | Vehicle crew training system |
CN101561972A (en) * | 2008-04-17 | 2009-10-21 | 北京宣爱智能模拟技术有限公司 | Method and system for interactive automobile driving case teaching |
CN101739855A (en) * | 2008-11-07 | 2010-06-16 | 北京宣爱智能模拟技术有限公司 | Automobilism network personalized teaching system and learning method thereof |
US20180277011A1 (en) * | 2011-10-31 | 2018-09-27 | Lifelong Driver Llc | Driver training |
CN103886054A (en) * | 2014-03-13 | 2014-06-25 | 中国科学院自动化研究所 | Personalization recommendation system and method of network teaching resources |
CN103971558A (en) * | 2014-05-16 | 2014-08-06 | 无锡海鸿信息技术有限公司 | Intelligent robot system for motor vehicle driver training |
US10115173B1 (en) * | 2014-09-23 | 2018-10-30 | Allstate Insurance Company | System and method for standardized evaluation of driver's license eligibility |
CN104618514A (en) * | 2015-03-05 | 2015-05-13 | 成都闰世科技有限公司 | Data processing method and device |
CN105808637A (en) * | 2016-02-23 | 2016-07-27 | 平安科技(深圳)有限公司 | Personalized recommendation method and device |
CN106548238A (en) * | 2016-02-24 | 2017-03-29 | 浙江你若信息科技有限公司 | One kind drives training reserving method and system |
CN105868317A (en) * | 2016-03-25 | 2016-08-17 | 华中师范大学 | Digital education resource recommendation method and system |
CN105844998A (en) * | 2016-04-25 | 2016-08-10 | 安徽嘻哈网络技术有限公司 | Electronic trainer teaching and testing system |
CN105824979A (en) * | 2016-06-07 | 2016-08-03 | 中国联合网络通信集团有限公司 | Course recommendation method and system of same |
CN106528812A (en) * | 2016-08-05 | 2017-03-22 | 浙江工业大学 | USDR model based cloud recommendation method |
CN106251724A (en) * | 2016-10-10 | 2016-12-21 | 北京高途智驾科技有限公司 | A kind of driver's intelligent tutoring robot system |
CN106528656A (en) * | 2016-10-20 | 2017-03-22 | 杨瀛 | Student history and real-time learning state parameter-based course recommendation realization method and system |
CN106779867A (en) * | 2016-12-30 | 2017-05-31 | 中国民航信息网络股份有限公司 | Support vector regression based on context-aware recommends method and system |
CN106651702A (en) * | 2016-12-30 | 2017-05-10 | 安徽宝信信息科技有限公司 | Intelligent voice driving training system |
CN106781876A (en) * | 2017-01-13 | 2017-05-31 | 南京多伦科技股份有限公司 | A kind of multifunctional intellectual vehicle-learning system |
CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
CN108510844A (en) * | 2018-04-16 | 2018-09-07 | 中国石油大学(华东) | A kind of full automatic vehicle driving instruction system |
CN108805308A (en) * | 2018-05-25 | 2018-11-13 | 王明文 | A kind of driving training APP practices reservation platform management system |
CN109543963A (en) * | 2018-11-06 | 2019-03-29 | 深圳信息职业技术学院 | A kind of big data analysis method and system based on student's study habit |
CN109492102A (en) * | 2018-11-08 | 2019-03-19 | 中国联合网络通信集团有限公司 | User data processing method, device, equipment and readable storage medium storing program for executing |
CN109543841A (en) * | 2018-11-09 | 2019-03-29 | 北京泊远网络科技有限公司 | Deep learning method, apparatus, electronic equipment and computer-readable medium |
Cited By (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110599853A (en) * | 2019-08-05 | 2019-12-20 | 深圳华桥智能设备科技有限公司 | Intelligent teaching system and method for driving school |
CN112819268A (en) * | 2019-11-15 | 2021-05-18 | 北京达佳互联信息技术有限公司 | Training information processing method, device, server and storage medium |
CN111008231A (en) * | 2019-11-22 | 2020-04-14 | 辰辰帮帮(北京)科技有限公司 | Operation method for training and examination data analysis in first-aid field |
CN110738881A (en) * | 2019-11-26 | 2020-01-31 | 国网冀北电力有限公司技能培训中心 | Power transformation operation simulation training system and method |
CN110969911A (en) * | 2019-12-05 | 2020-04-07 | 珠海超凡视界科技有限公司 | Intelligent subject two training system and method based on virtual reality |
CN111932414A (en) * | 2020-08-07 | 2020-11-13 | 泰康保险集团股份有限公司 | Training management system and method, computer storage medium and electronic equipment |
CN112396913A (en) * | 2020-10-12 | 2021-02-23 | 易显智能科技有限责任公司 | Method and related device for recording and authenticating training duration of motor vehicle driver |
CN112053610A (en) * | 2020-10-29 | 2020-12-08 | 延安大学 | VR virtual driving training and examination method based on deep learning |
CN112183899A (en) * | 2020-11-04 | 2021-01-05 | 北京嘀嘀无限科技发展有限公司 | Method, device, equipment and storage medium for determining safety degree prediction model |
CN112905758A (en) * | 2021-01-29 | 2021-06-04 | 深圳通联金融网络科技服务有限公司 | Intelligent training management method and system based on telephone robot |
CN113160646A (en) * | 2021-04-14 | 2021-07-23 | 国家电网有限公司 | Wired access network simulation training oriented trainee model construction and updating method |
EP3998595A3 (en) * | 2021-06-18 | 2022-10-12 | Apollo Intelligent Connectivity (Beijing) Technology Co., Ltd. | Method and apparatus for adjusting driving training course |
CN113362674A (en) * | 2021-06-18 | 2021-09-07 | 北京百度网讯科技有限公司 | Method and device for adjusting driving training course, electronic equipment and storage medium |
CN113496634A (en) * | 2021-07-20 | 2021-10-12 | 广州星锐传媒信息科技有限公司 | Simulated driving training method based on one-way video interaction |
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 |
CN114548713A (en) * | 2022-02-10 | 2022-05-27 | 北方天途航空技术发展(北京)有限公司 | Training result intelligent management method, system and storage medium |
WO2023168517A1 (en) * | 2022-03-07 | 2023-09-14 | Cae Inc. | Adaptive learning in a diverse learning ecosystem |
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