CN109741629A - The real-time construction method of user's portrait, system, computer equipment and storage medium - Google Patents
The real-time construction method of user's portrait, system, computer equipment and storage medium Download PDFInfo
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- CN109741629A CN109741629A CN201811573293.7A CN201811573293A CN109741629A CN 109741629 A CN109741629 A CN 109741629A CN 201811573293 A CN201811573293 A CN 201811573293A CN 109741629 A CN109741629 A CN 109741629A
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Abstract
The embodiment of the invention provides a kind of real-time construction methods of portrait, which comprises obtains the log information of user, the log information is the vehicle operation data that user currently drives vehicle；The log information is filtered, to obtain the critical field of the log information；Extract the vehicle operation critical data in the critical field；Critical data is run according to the vehicle, analyzes the vehicle drive exception classification and the corresponding frequency of occurrence of each vehicle drive exception classification that the vehicle occurs in the process of moving；According to each vehicle drive exception classification and the corresponding frequency of occurrence of each vehicle drive exception classification, user's portrait is constructed to vehicle driver.The present embodiment can determine whether to send out early stage warning information, attenuating accident rate according to user's portrait.The present embodiment also achieves after the real-time logs information for obtaining user, and the user for just rebuilding vehicle driver draws a portrait in real time, improves to vehicle driver user's portrait accuracy.
Description
Technical field
The present embodiments relate to field of computer data processing more particularly to a kind of user draw a portrait real-time construction method,
System, computer equipment and computer readable storage medium.
Background technique
With the increase year by year of vehicle population, the traffic accident frequently occurred seriously threatens the lives and properties peace of people
Entirely.To reduce traffic accident incidence, traditional solution is: it is sounded an alarm in the forward direction driver of traffic accident generation,
Driver is reminded to take certain Anticollision Measures, therefore, lot of domestic and international mechanism is all in research car crass early warning system, to mention
The active safety of high running car.
However, above-mentioned solution is to issue emergency alert information when causing danger situation between vehicle, it is unable to
Issue early stage warning information, i.e., cannot between vehicle there are no cause danger situation when, issue certain warning information.
Summary of the invention
In view of this, the purpose of the embodiment of the present invention is that providing a kind of real-time construction method, system, computer equipment of drawing a portrait
And computer readable storage medium, solve can not between vehicle there are no issued before situation of causing danger early stage accuse
The problem of alert information.
To achieve the above object, the embodiment of the invention provides a kind of real-time construction methods of portrait, comprising the following steps:
The log information of user is obtained, the log information includes the vehicle operation data that user currently drives vehicle；
The log information is filtered, to obtain the critical field of the log information；
The vehicle operation critical data in the critical field is extracted, the vehicle operation critical data includes traveling speed
Degree, whistle number and/or steering data；
Critical data is run according to the vehicle, analyzes the vehicle drive exception class that the vehicle occurs in the process of moving
Mesh and the corresponding frequency of occurrence of each vehicle drive exception classification, the vehicle drive exception classification include emergency brake class
Mesh, urgent speed change classification, racing are blown a whistle classification to classification and/or frequently；And
It is right according to each vehicle drive exception classification and the corresponding frequency of occurrence of each vehicle drive exception classification
Vehicle driver constructs user's portrait.
Preferably, the log information is filtered, the step of to obtain the critical field of the log information, packet
It includes:
It is concentrated according to preset multiple tag identifiers from key character and searches multiple key-strings, according to the multiple pass
Multiple critical fielies in log information described in key characters String matching；
Wherein, the corresponding one or more key-strings of each tag identifier.
Preferably, according to each vehicle drive exception classification and the corresponding appearance of each vehicle drive exception classification
Number constructs the step of user draws a portrait to vehicle driver, comprising:
It is driven according to each corresponding default driving safety coefficient of vehicle drive exception classification and each vehicle
The corresponding frequency of occurrence of abnormal classification is sailed, comprehensive driving safety coefficient is calculated；
User's portrait of the vehicle driver is determined according to the comprehensive driving safety coefficient.
Preferably, the step of user's portrait of the vehicle driver is determined according to the comprehensive driving safety coefficient, packet
It includes:
The driving grade of the vehicle driver is defined according to the comprehensive driving safety system；
Judge whether the driving grade is lower than normal driving grade multiple grades, the normal driving grade is described drives
The multiple history for the person of sailing drive the average driving grade of grade；And
If the driving grade is less than normal driving grade multiple grades, it is determined that the vehicle driver is in liquor-saturated
Wine state.
Preferably, the step of user's portrait of the vehicle driver is determined according to the comprehensive driving safety coefficient, packet
It includes:
The driving grade of the vehicle driver is defined according to the comprehensive driving safety system；
Judge whether the driving grade is normal driving grade, and the driving grade corresponds to dangerous driving rank；
And
If the driving grade is normal driving grade, and the driving grade corresponds to dangerous driving rank, then really
The fixed vehicle driver is in new hand's state.
Preferably, according to each vehicle drive exception classification and the corresponding appearance of each vehicle drive exception classification
Number constructs the step of user draws a portrait to vehicle driver, comprising:
An input vector is defined according to by the corresponding frequency of occurrence of each abnormal classification；
The input vector is input in neural network, the prediction number of each user's portrait classification predetermined is obtained
According to；And
The maximum user's portrait classification of prediction data is determined as user's portrait.
Preferably, according to each vehicle drive exception classification and corresponding vehicle drive safety coefficient, vehicle is driven
The person of sailing constructed after the step of user's portrait, comprising:
It is drawn a portrait according to the user and executes corresponding operation strategy:
It is drawn a portrait according to the user, defines warning level；And
When the warning level is higher than predetermined level, issued and the warning level by car networking to surrounding vehicles
The information warning matched.
To achieve the above object, the embodiment of the invention also provides portraits to construct system in real time, comprising:
Module is obtained, for obtaining the log information of user, the log information includes the vehicle that user currently drives vehicle
Operation data；
Filtering module, for being filtered to the log information, to obtain the critical field of the log information；
Extraction module, for extracting the operation critical data of the vehicle in the critical field, the vehicle runs crucial number
According to including travel speed, whistle number and/or steering data；
Analysis module analyzes what the vehicle occurred in the process of moving for running critical data according to the vehicle
Vehicle drive exception classification and the corresponding frequency of occurrence of each vehicle drive exception classification, the vehicle drive exception classification packet
Emergency brake classification, urgent speed change classification, racing is included to blow a whistle to classification and/or frequently classification；And
Portrait module, for corresponding according to each vehicle drive exception classification and each vehicle drive exception classification
Frequency of occurrence, to vehicle driver construct user portrait.
To achieve the above object, the embodiment of the invention also provides a kind of computer equipment, the computer equipment storages
Device, processor and it is stored in the computer program that can be run on the memory and on the processor, the computer journey
The step of drawing a portrait real-time construction method as described above is realized when sequence is executed by processor.
To achieve the above object, the embodiment of the invention also provides a kind of computer readable storage medium, the computers
Computer program is stored in readable storage medium storing program for executing, the computer program can be performed by least one processor, so that institute
It states at least one processor and executes the step of drawing a portrait real-time construction method as described above.
Real-time construction method, system, computer equipment and the computer-readable storage medium provided in an embodiment of the present invention of drawing a portrait
Matter currently drives from user and extracts vehicle operation critical data in the vehicle operation data of vehicle, crucial based on vehicle operation
Data analyze the vehicle drive exception classification of vehicle, and construct user's portrait to vehicle driver based on the analysis result, thus
It can be determined whether to send out early stage warning information according to user's portrait, lower accident rate.In addition, the present embodiment is to real-time
The log information of acquisition is filtered, and is extracted vehicle by critical field and is run critical data, and then determines that driver user draws
Picture realizes after a real-time logs information for obtaining user, and the user for just rebuilding vehicle driver draws a portrait in real time, mentions
It has risen to vehicle driver user's portrait accuracy.
Detailed description of the invention
Fig. 1 is the flow diagram of the real-time construction method embodiment one of present invention portrait.
Fig. 2 is the flow diagram of step S108 in Fig. 1.
Fig. 3 is another flow diagram of the real-time construction method embodiment one of present invention portrait.
Fig. 4 is the flow diagram of step S110 in Fig. 3.
Fig. 5 is the flow diagram of the real-time construction method embodiment two of present invention portrait.
Fig. 6 is the program module schematic diagram that present invention portrait constructs system embodiment three in real time.
Fig. 7 is the hardware structural diagram of computer equipment example IV of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.Based on the embodiments of the present invention, those of ordinary skill in the art are not before making creative work
Every other embodiment obtained is put, shall fall within the protection scope of the present invention.
It should be noted that the description for being related to " first ", " second " etc. in the present invention is used for description purposes only, and cannot
It is interpreted as its relative importance of indication or suggestion or implicitly indicates the quantity of indicated technical characteristic.Define as a result, " the
One ", the feature of " second " can explicitly or implicitly include at least one of the features.In addition, the skill between each embodiment
Art scheme can be combined with each other, but must be based on can be realized by those of ordinary skill in the art, when technical solution
Will be understood that the combination of this technical solution is not present in conjunction with there is conflicting or cannot achieve when, also not the present invention claims
Protection scope within.
Following embodiment will be that executing subject carries out exemplary description with computer equipment 2.
Embodiment one
Refering to fig. 1, the step flow chart of the real-time construction method of portrait of the embodiment of the present invention one is shown.It is appreciated that
Flow chart in this method embodiment, which is not used in, is defined the sequence for executing step.It is specific as follows.
Step S100, obtains the log information of user, and the log information includes the vehicle fortune that user currently drives vehicle
Row data.
Illustratively, connection vehicle assembly can be passed through by board diagnosis system (OBD, on-borad diagnostics)
In each control system and each induction system, read each control system and induction system (e.g., engine control system in real time
System, control system of speed variator, vehicle wire-controlled steering system, brake control system, instrument disk control system, velocity response system
Etc.) operation subdata.
Illustratively, CAN bus data can be read by board diagnosis system, to obtain each control system in real time
Operation subdata.CAN (Controller Area Network, the controller LAN) bus is used for each control mould
Various types of data required for block interaction.
Illustratively, some operation subnumbers in the vehicle operation data can be obtained by other external sensing devices
According to, such as automobile data recorder.
Step S102 is filtered the log information, to obtain the critical field of the log information.
Multiple key-strings are searched specifically, concentrating according to preset multiple tag identifiers from key character, according to institute
State multiple critical fielies in log information described in multiple key character String matchings, wherein each tag identifier it is corresponding one or
Multiple key-strings.
Such as:
Tag identifier #1: speed mark, corresponding one or more character string may be " speed ", " rate " etc., the mark
The associated operation subdata of label mark are as follows: speed data；
Tag identifier #2: turning to mark, and corresponding one or more character string may be " circle " etc., the tag identifier
Associated operation subdata are as follows: turn to data；
Tag identifier #3: loudspeaker mark, corresponding one or more character string may be " speaker " etc., the label mark
Know associated operation subdata are as follows: whistle data.
These tag identifiers be it is predefined, according to user need and select, for example, tag identifier can also include antilock
Dead marking etc., each tag identifier can be associated with multiple character strings.For example, monitoring velocity data may have multiple data sources,
Such as come from: velocity response system, automobile data recorder, field name corresponding to the field of the two data source writing speed data can
It can be not identical；If necessary to extract the speed data of the two data sources, then need to select to be respectively associated by speed tag
Field name of the two data sources for the field of writing speed data.
Step S104, extracts the vehicle operation critical data in the critical field, and the vehicle runs critical packet
It includes travel speed, whistle number and/or turns to data.
Travel speed: on board diagnosis system can by least one of velocity response system, control system of speed variator,
To obtain the travel speed.Whistle data: on board diagnosis system can monitor horn for vehicle system, to obtain whistle event.
Specifically, can generate a trigger signal when horn for vehicle key is triggered and be transmitted to horn for vehicle by the CAN bus
Control system executes whistle operation by horn for vehicle control system, and on board diagnosis system can monitor horn for vehicle control system
Or CAN bus with the extract real-time trigger signal and is recorded whistle event.Turn to data: on board diagnosis system can monitor vehicle
Wire-controlled steering system or steering wheel induction system, to obtain steering data.
Step S106 runs critical data according to the vehicle, analyzes the vehicle that the vehicle occurs in the process of moving
Drive abnormal classification and the corresponding frequency of occurrence of each vehicle drive exception classification.
The vehicle drive exception classification is abnormal classification predetermined, comprising: emergency brake classification, urgent speed change class
Mesh, racing to classification, frequent whistle classification etc..
Emergency brake classification: the emergency brake event in the analysis information record time: (1) pass through brake and drive system for vehicle with tow, brake
Vehicle auxiliary system or ABS (anti-lock braking system), brake inductor etc., monitor and whether there is emergency brake event and emergency brake
Number.(2) speed data in the analysis information record time, if there is the speed in prefixed time interval near general 0, and
Velocity variations in the prefixed time interval are greater than preset value, if there is being then determined as emergency brake event.And on this basis,
Obtain the number of the emergency brake event in the record time.
Urgent speed change classification: with reference to the judgment mode of emergency parameter classification.
Racing is to classification: the racing in the analysis information record time is to event: (1) analysis information records the vehicle in the time
Speed obtains steering wheel angle when driving vehicle turning, and the radius of curvature and driving vehicle when being turned according to driving vehicle turn
The mapping relations of steering wheel angle when curved, steering wheel angle when turning under driving vehicle arbitrary speed overstep the extreme limit steering wheel
When angle, racing is determined that it is to event.(2) angular speed for obtaining driving vehicle is preset angular speed when angular speed is greater than, is then sentenced
Surely there is racing to event.
Frequent whistle classification: the duration and/or number of the whistle event in the analysis information record time, when being more than default ring
When flute number, then frequent whistle event is defined as.
Step S108, it is corresponding out according to each vehicle drive exception classification and each vehicle drive exception classification
Occurrence number constructs user's portrait to vehicle driver.
As shown in Fig. 2, the step S108 can specifically include step S108a~S108b:
Step S108a, according to the corresponding default driving safety coefficient of each vehicle drive exception classification and described
The corresponding frequency of occurrence of each vehicle drive exception classification calculates comprehensive driving safety coefficient.
Step S108b determines that the user of the vehicle driver draws a portrait according to the comprehensive driving safety coefficient.
It is illustrative: the driving grade of the vehicle driver is defined according to the comprehensive driving safety system；Judge institute
It states and drives whether grade is lower than normal driving grade multiple grades, the normal driving grade is multiple history of the driver
Drive the average driving grade of grade；If the driving grade is less than normal driving grade multiple grades, it is determined that described
Vehicle driver is in state of intoxication.
It is illustrative: the driving grade of the vehicle driver is defined according to the comprehensive driving safety system；Judge institute
It states and drives whether grade is normal driving grade, and the driving grade corresponds to dangerous driving rank；If described driving etc.
Grade is normal driving grade, and the driving grade corresponds to dangerous driving rank, it is determined that is defined at the vehicle driver
In new hand's state.
Optionally, it as shown in figure 3, the method also includes step S110, is drawn a portrait according to the user and executes corresponding behaviour
Make strategy.As shown in figure 4, the step S110 can specifically include step S110a~S110b:
Step S110a draws a portrait according to the user, defines warning level；And
Step S110b, when the warning level be higher than predetermined level when, by car networking to surrounding vehicles issue with it is described
The matched information warning of warning level.
It can be appreciated that active user portrait is constructed for vehicle driver, it can there are no feelings of causing danger between vehicle
When condition, certain warning information is issued.
Embodiment two
Refering to Fig. 5, the step flow chart of the real-time construction method of portrait of the embodiment of the present invention two is shown.It is appreciated that
Flow chart in this method embodiment, which is not used in, is defined the sequence for executing step.It is specific as follows.
Step S200, obtains the log information of user, and the log information includes the vehicle fortune that user currently drives vehicle
Row data.
Step S202 is filtered the log information, to obtain the critical field of the log information.
Step S204, extracts the vehicle operation critical data in the critical field, and the vehicle runs critical packet
It includes travel speed, whistle number and/or turns to data.
Step S106 runs critical data according to the vehicle, analyzes the vehicle that the vehicle occurs in the process of moving
Drive abnormal classification and the corresponding frequency of occurrence of each vehicle drive exception classification.
Step S208 defines an input vector according to by the corresponding frequency of occurrence of each abnormal classification.
Illustratively, pre-define n abnormal classification, such as abnormal classification 1, abnormal classification 2, abnormal classification 3 ..., exception
Classification n, and a n-dimensional vector is configured with this n abnormal classification, each input vector parameter corresponding one in the n-dimensional vector
Exception class purpose frequency of occurrence.
The input vector is input in neural network by step S210, obtains each user's portrait class predetermined
Other prediction data.
The neural network can be shot and long term Memory Neural Networks after training, for the shot and long term Memory Neural Networks
It exports corresponding class categories one and shares m user's portrait classification.The m of the shot and long term Memory Neural Networks ties up output vector,
Each output vector parameter is used to indicate that the prediction data of one of user's portrait classification, the prediction data also referred to as to be set
Reliability.
The maximum user's portrait classification of prediction data is determined as the user and drawn a portrait by step S212.
Such as:
First user portrait: the driving condition of vehicle driver is good, confidence level 0.55；
Second user portrait: the driving condition of vehicle driver is general, confidence level 0.70；
Third user portrait: the driving condition of vehicle driver is dangerous, confidence level 0.80；
Fourth user portrait: the driving condition of vehicle driver is abnormally dangerous (being equivalent under state of intoxication), and confidence level is
0.95；
User portrait classification is then determined as fourth user portrait.
Embodiment three
Please continue to refer to Fig. 6, the program module schematic diagram that present invention portrait constructs system embodiment three in real time is shown.?
In the present embodiment, real-time building system 20 of drawing a portrait may include or be divided into one or more program modules, and one or more
A program module is stored in storage medium, and as performed by one or more processors, to complete the present invention, and can be realized
The above-mentioned real-time construction method of portrait.The so-called program module of the embodiment of the present invention is a series of meters for referring to complete specific function
Calculation machine program instruction section draws a portrait more suitable for description than program itself and constructs execution of the system 20 in storage medium in real time
Journey.The function of each program module of the present embodiment will specifically be introduced by being described below:
Module 200 is obtained, for obtaining the log information of user, the log information includes that user currently drives vehicle
Vehicle operation data.
Illustratively, obtaining module 200 can be logical by board diagnosis system (OBD, on-borad diagnostics)
Each control system in connection vehicle assembly and each induction system are crossed, reads each control system and induction system in real time
(e.g., engine control system, control system of speed variator, vehicle wire-controlled steering system, brake control system, instrument board control
System, velocity response system etc.) operation subdata.
Illustratively, CAN bus data can be read by board diagnosis system by obtaining module 200, to obtain in real time
The operation subdata of each control system.CAN (Controller Area Network, the controller LAN) bus is used
The Various types of data required for the interaction of each control module.
Illustratively, obtaining module 200 can be obtained in the vehicle operation data by other external sensing devices
Some operation subdatas, such as automobile data recorder.
Filtering module 202, for being filtered to the log information, to obtain the critical field of the log information.
Illustratively, the filtering module 202 can be concentrated from key character according to preset multiple tag identifiers and be searched
Multiple key-strings, according to multiple critical fielies in log information described in the multiple key character String matching, wherein every
The corresponding one or more key-strings of a tag identifier.
Such as:
Tag identifier #1: speed mark, corresponding one or more character string may be " speed ", " rate " etc., the mark
The associated operation subdata of label mark are as follows: speed data；
Tag identifier #2: turning to mark, and corresponding one or more character string may be " circle " etc., the tag identifier
Associated operation subdata are as follows: turn to data；
Tag identifier #3: loudspeaker mark, corresponding one or more character string may be " speaker " etc., the label mark
Know associated operation subdata are as follows: whistle data.
These tag identifiers be it is predefined, according to user need and select, for example, tag identifier can also include antilock
Dead marking etc., each tag identifier can be associated with multiple character strings.For example, monitoring velocity data may have multiple data sources,
Such as come from: velocity response system, automobile data recorder, field name corresponding to the field of the two data source writing speed data can
It can be not identical；If necessary to extract the speed data of the two data sources, then need to select to be respectively associated by speed tag
Field name of the two data sources for the field of writing speed data.
Extraction module 204, for extracting the operation critical data of the vehicle in the critical field, the vehicle operation is crucial
Data include travel speed, whistle number and/or steering data.
Travel speed: on board diagnosis system can by least one of velocity response system, control system of speed variator,
To obtain the travel speed.Whistle data: on board diagnosis system can monitor horn for vehicle system, to obtain whistle event.
Specifically, can generate a trigger signal when horn for vehicle key is triggered and be transmitted to horn for vehicle by the CAN bus
Control system executes whistle operation by horn for vehicle control system, and on board diagnosis system can monitor horn for vehicle control system
Or CAN bus with the extract real-time trigger signal and is recorded whistle event.Turn to data: on board diagnosis system can monitor vehicle
Wire-controlled steering system or steering wheel induction system, to obtain steering data.
Analysis module 206 is analyzed the vehicle and is occurred in the process of moving for running critical data according to the vehicle
Vehicle drive exception classification and the corresponding frequency of occurrence of each vehicle drive exception classification.
The vehicle drive exception classification is abnormal classification predetermined, comprising: emergency brake classification, urgent speed change class
Mesh, racing to classification, frequent whistle classification etc..Emergency brake classification: the emergency brake event in the analysis information record time:
(1) by brake and drive system for vehicle with tow, brake assist system or ABS (anti-lock braking system), brake inductor etc., monitoring whether there is
Emergency brake event and emergency brake number.(2) speed data in the analysis information record time, if having prefixed time interval
Interior speed near general 0, and the velocity variations in the prefixed time interval are greater than preset value, it is urgent if there is being then determined as
Braking events.And on this basis, the number of the emergency brake event in the record time is obtained.Urgent speed change classification: ginseng
Examine the judgment mode of emergency parameter classification.Racing is to classification: the racing in the analysis information record time is to event: (1) analysis is believed
Car speed in the breath record time obtains steering wheel angle when driving vehicle turning, song when turning according to driving vehicle
The mapping relations of steering wheel angle when rate radius and driving vehicle are turned, steering wheel when turning under driving vehicle arbitrary speed
Angle overstep the extreme limit steering wheel angle when, determine that it is racing to event.(2) angular speed for obtaining driving vehicle, works as angular speed
Greater than default angular speed, then determine that there are racings to event.Frequent whistle classification: the whistle event in the analysis information record time
Number be then defined as frequent whistle event when being more than default whistle number.
Portrait module 208, for according to each vehicle drive exception classification and each vehicle drive exception classification
Corresponding frequency of occurrence constructs user's portrait to vehicle driver.
In one embodiment:
Portrait module 208 is used for: according to the corresponding default driving safety coefficient of each vehicle drive exception classification, with
And the corresponding frequency of occurrence of each vehicle drive exception classification, calculate comprehensive driving safety coefficient；It is driven according to the synthesis
Sail user's portrait that safety coefficient determines the vehicle driver.
It is illustrative: the driving grade of the vehicle driver is defined according to the comprehensive driving safety system；Judge institute
It states and drives whether grade is lower than normal driving grade multiple grades, the normal driving grade is multiple history of the driver
Drive the average driving grade of grade；If the driving grade is less than normal driving grade multiple grades, it is determined that described
Vehicle driver is in state of intoxication.
It is illustrative: the driving grade of the vehicle driver is defined according to the comprehensive driving safety system；Judge institute
It states and drives whether grade is normal driving grade, and the driving grade corresponds to dangerous driving rank；If described driving etc.
Grade is normal driving grade, and the driving grade corresponds to dangerous driving rank, it is determined that is defined at the vehicle driver
In new hand's state.
In another embodiment:
Portrait module 208 is used for: according to will one input vector of each abnormal corresponding frequency of occurrence definition of classification；
The input vector is input in neural network, the prediction data of each user's portrait classification predetermined is obtained；It will be pre-
The maximum user's portrait classification of measured data is determined as user's portrait.
Illustratively, pre-define n abnormal classification, such as abnormal classification 1, abnormal classification 2, abnormal classification 3 ..., exception
Classification n, and a n-dimensional vector is configured with this n abnormal classification, each input vector parameter corresponding one in the n-dimensional vector
Exception class purpose frequency of occurrence.
The neural network can be shot and long term Memory Neural Networks after training, for the shot and long term Memory Neural Networks
It exports corresponding class categories one and shares m user's portrait classification.The m of the shot and long term Memory Neural Networks ties up output vector,
Each output vector parameter is used to indicate that the prediction data of one of user's portrait classification, the prediction data also referred to as to be set
Reliability.
Such as:
First user portrait: the driving condition of vehicle driver is good, confidence level 0.55；
Second user portrait: the driving condition of vehicle driver is general, confidence level 0.70；
Third user portrait: the driving condition of vehicle driver is dangerous, confidence level 0.80；
Fourth user portrait: the driving condition of vehicle driver is abnormally dangerous (being equivalent under state of intoxication), and confidence level is
0.95；
User portrait classification is then determined as fourth user portrait.
Optionally, further include alarm module 210, execute corresponding operation strategy for drawing a portrait according to the user, specifically
It is as follows: to be drawn a portrait according to the user, define warning level；When the warning level be higher than predetermined level when, by car networking to
Surrounding vehicles issue and the matched information warning of the warning level.
It can be appreciated that active user portrait is constructed for vehicle driver, it can there are no feelings of causing danger between vehicle
When condition, certain warning information is issued.
Example IV
It is the hardware structure schematic diagram of the computer equipment of the embodiment of the present invention four refering to Fig. 7.It is described in the present embodiment
Computer equipment 2 is that one kind can be automatic to carry out numerical value calculating and/or information processing according to the instruction for being previously set or storing
Equipment.The computer equipment 2 can be vehicle electronic device, rack-mount server, blade server, tower server or
Cabinet-type server (including server cluster composed by independent server or multiple servers) etc..As shown, institute
It states computer equipment 2 to include at least, but is not limited to, connection memory 21, processor 22, net can be in communication with each other by system bus
Network interface 23 and portrait construct system 20 in real time.Wherein:
In the present embodiment, memory 21 includes at least a type of computer readable storage medium, the readable storage
Medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory etc.), random access storage device
(RAM), static random-access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory
(EEPROM), programmable read only memory (PROM), magnetic storage, disk, CD etc..In some embodiments, memory
21 can be the internal storage unit of computer equipment 2, such as the hard disk or memory of the computer equipment 2.In other implementations
In example, memory 21 is also possible to the grafting being equipped on the External memory equipment of computer equipment 2, such as the computer equipment 20
Formula hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card
(Flash Card) etc..Certainly, memory 21 can also both including computer equipment 2 internal storage unit and also including outside it
Store equipment.In the present embodiment, memory 21 is installed on the operating system and types of applications of computer equipment 2 commonly used in storage
Software, such as the real-time program code etc. for constructing system 20 of portrait of embodiment five.In addition, memory 21 can be also used for temporarily
Ground stores the Various types of data that has exported or will export.
Processor 22 can be in some embodiments central processing unit (Central Processing Unit, CPU),
Controller, microcontroller, microprocessor or other data processing chips.The processor 22 is commonly used in control computer equipment 2
Overall operation.In the present embodiment, program code or processing data of the processor 22 for being stored in run memory 21, example
Such as run the real-time building system 20 of portrait, the real-time construction method of portrait to realize embodiment one or two.
The network interface 23 may include radio network interface or wired network interface, which is commonly used in
Communication connection is established between the computer equipment 2 and other electronic devices.For example, the network interface 23 is for passing through network
The computer equipment 2 is connected with exterior terminal, establishes data transmission between the computer equipment 2 and exterior terminal
Channel and communication connection etc..The network can be intranet (Intranet), internet (Internet), whole world movement
Communication system (Global System of Mobile communication, GSM), wideband code division multiple access (Wideband
Code Division Multiple Access, WCDMA), 4G network, 5G network, bluetooth (Bluetooth), the nothings such as Wi-Fi
Line or cable network.
It should be pointed out that Fig. 7 illustrates only the computer equipment 2 with component 20-23, it should be understood that simultaneously
All components shown realistic are not applied, the implementation that can be substituted is more or less component.
In the present embodiment, the portrait being stored in memory 21 constructs system 20 in real time can also be divided into one
A or multiple program modules, one or more of program modules are stored in memory 21, and by one or more
Processor (the present embodiment is processor 22) is performed, to complete the present invention.
For example, Fig. 6 shows the program module schematic diagram for realizing real-time 20 embodiment three of building system of drawing a portrait, the reality
It applies in example, described can be divided into based on real-time building system 20 of drawing a portrait obtains module 200, filtering module 202, extraction module
204, analysis module 206, portrait module 208 and alarm module 210.Wherein, the so-called program module of the present invention is to have referred to
At the series of computation machine program instruction section of specific function, than program, more suitable for describing the portrait, building system 20 exists in real time
Implementation procedure in the computer equipment 2.The concrete function of described program module 200-210 has detailed in example IV
Description, details are not described herein.
Embodiment five
The present embodiment also provides a kind of computer readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory
(for example, SD or DX memory etc.), random access storage device (RAM), static random-access memory (SRAM), read-only memory
(ROM), electrically erasable programmable read-only memory (EEPROM), programmable read only memory (PROM), magnetic storage, magnetic
Disk, CD, server, App are stored thereon with computer program, phase are realized when program is executed by processor using store etc.
Answer function.The computer readable storage medium of the present embodiment is for storing portrait building system 20 in real time, when being executed by processor
Realize the real-time construction method of portrait of embodiment one or two.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
- The real-time construction method 1. a kind of user draws a portrait, which is characterized in that the described method includes:The log information of user is obtained, the log information includes the vehicle operation data that user currently drives vehicle；The log information is filtered, to obtain the critical field of the log information；The vehicle operation critical data in the critical field is extracted, the vehicle operation critical data includes travel speed, ring Flute number and/or steering data；According to the vehicle run critical data, analyze the vehicle drive exception classification that the vehicle occurs in the process of moving with And the corresponding frequency of occurrence of each vehicle drive exception classification, the vehicle drive exception classification includes emergency brake classification, tight Sudden turn of events speed classification, racing are blown a whistle classification to classification and/or frequently；AndAccording to each vehicle drive exception classification and the corresponding frequency of occurrence of each vehicle drive exception classification, to vehicle Driver constructs user's portrait.
- The real-time construction method 2. user according to claim 1 draws a portrait, which is characterized in that the log information was carried out Filter, the step of to obtain the critical field of the log information, comprising:It is concentrated according to preset multiple tag identifiers from key character and searches multiple key-strings, according to the multiple keyword Accord with multiple critical fielies in log information described in String matching；Wherein, the corresponding one or more key-strings of each tag identifier.
- The real-time construction method 3. user according to claim 2 draws a portrait, which is characterized in that according to each vehicle drive Abnormal classification and the corresponding frequency of occurrence of each vehicle drive exception classification construct the step of user's portrait to vehicle driver Suddenly, comprising:It is different according to each corresponding default driving safety coefficient of vehicle drive exception classification and each vehicle drive The corresponding frequency of occurrence of normal classification calculates comprehensive driving safety coefficient；User's portrait of the vehicle driver is determined according to the comprehensive driving safety coefficient.
- The real-time construction method 4. user according to claim 3 draws a portrait, which is characterized in that according to the comprehensive driving safety Coefficient determines the step of user's portrait of the vehicle driver, comprising:The driving grade of the vehicle driver is defined according to the comprehensive driving safety system；Judge whether the driving grade is lower than normal driving grade multiple grades, the normal driving grade is the driver Multiple history drive grade average driving grade；AndIf the driving grade is less than normal driving grade multiple grades, it is determined that the vehicle driver is in drunk shape State.
- The real-time construction method 5. user according to claim 3 draws a portrait, which is characterized in that according to the comprehensive driving safety Coefficient determines the step of user's portrait of the vehicle driver, comprising:The driving grade of the vehicle driver is defined according to the comprehensive driving safety system；Judge whether the driving grade is normal driving grade, and the driving grade corresponds to dangerous driving rank；AndIf the driving grade is normal driving grade, and the driving grade corresponds to dangerous driving rank, it is determined that institute It states vehicle driver and is in new hand's state.
- The real-time construction method 6. user according to claim 1 draws a portrait, which is characterized in that according to each vehicle drive Abnormal classification and the corresponding frequency of occurrence of each vehicle drive exception classification construct the step of user's portrait to vehicle driver Suddenly, comprising:An input vector is defined according to by the corresponding frequency of occurrence of each abnormal classification；The input vector is input in neural network, the prediction data of each user's portrait classification predetermined is obtained； AndThe maximum user's portrait classification of prediction data is determined as user's portrait.
- 7. the real-time construction method of the portrait of user described in any one according to claim 1~6, which is characterized in that according to described Each vehicle drive exception classification and corresponding vehicle drive safety coefficient, to vehicle driver construct user portrait the step of it Afterwards, comprising:It is drawn a portrait according to the user and executes corresponding operation strategy:It is drawn a portrait according to the user, defines warning level；AndIt is matched with the warning level to surrounding vehicles sending by car networking when the warning level is higher than predetermined level Information warning.
- 8. a kind of user's portrait constructs system in real time characterized by comprisingModule is obtained, for obtaining the log information of user, the log information includes the vehicle fortune that user currently drives vehicle Row data；Filtering module, for being filtered to the log information, to obtain the critical field of the log information；Extraction module, for extracting the operation critical data of the vehicle in the critical field, the vehicle runs critical packet It includes travel speed, whistle number and/or turns to data；Analysis module analyzes the vehicle that the vehicle occurs in the process of moving for running critical data according to the vehicle Abnormal classification and the corresponding frequency of occurrence of each vehicle drive exception classification are driven, the vehicle drive exception classification includes tight Bring to a halt classification, urgent speed change classification, racing is blown a whistle classification to classification and/or frequently；AndPortrait module, for corresponding out according to each vehicle drive exception classification and each vehicle drive exception classification Occurrence number constructs user's portrait to vehicle driver.
- 9. a kind of computer equipment, the computer equipment memory, processor and it is stored on the memory and can be in institute State the computer program run on processor, which is characterized in that such as right is realized when the computer program is executed by processor It is required that the step of real-time construction method of portrait described in any one of 1 to 7.
- 10. a kind of computer readable storage medium, which is characterized in that be stored with computer in the computer readable storage medium Program, the computer program can be performed by least one processors, so that at least one described processor executes such as right It is required that the step of real-time construction method of portrait described in any one of 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110766826A (en) * | 2019-09-10 | 2020-02-07 | 中国平安财产保险股份有限公司 | Driving behavior analysis method |
| CN111210620A (en) * | 2019-12-19 | 2020-05-29 | 广州航天海特系统工程有限公司 | Method, device and equipment for generating driver portrait and storage medium |
| CN112598905A (en) * | 2020-12-14 | 2021-04-02 | 苏州智能交通信息科技股份有限公司 | Passenger flow dynamic analysis early warning method, system and storage medium |
| WO2022105111A1 (en) * | 2020-11-17 | 2022-05-27 | 平安科技（深圳）有限公司 | Regional profile generation method and apparatus, computer device, and storage medium |
| CN114694412A (en) * | 2020-12-25 | 2022-07-01 | 腾讯科技（深圳）有限公司 | Vehicle safety management control method, device and system and electronic equipment |
| CN115497295A (en) * | 2022-09-21 | 2022-12-20 | 联通智网科技股份有限公司 | Safety early warning method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101169873A (en) * | 2007-11-26 | 2008-04-30 | 电子科技大学 | Abnormal driving intelligent checking system and checking method |
| CN102428504A (en) * | 2009-05-20 | 2012-04-25 | 欧宝士株式会社 | Driving assistance method and device |
| CN103198685A (en) * | 2013-03-15 | 2013-07-10 | Tcl集团股份有限公司 | Method and system for achieving driving safety early warning |
| WO2014109973A1 (en) * | 2013-01-10 | 2014-07-17 | International Business Machines Corporation | Automatic driver modeling for integration of human-controlled vehicles into an autonomous vehicle network |
| CN104090886A (en) * | 2013-12-09 | 2014-10-08 | 深圳市腾讯计算机系统有限公司 | Method and device for constructing real-time portrayal of user |
| CN104732785A (en) * | 2015-01-09 | 2015-06-24 | 杭州好好开车科技有限公司 | Driving behavior analyzing and reminding method and system |
| CN106530828A (en) * | 2016-12-07 | 2017-03-22 | 深圳市元征科技股份有限公司 | Vehicle driving state early-warning method and device |
| CN107066616A (en) * | 2017-05-09 | 2017-08-18 | 北京京东金融科技控股有限公司 | Method, device and electronic equipment for account processing |
| CN108289108A (en) * | 2016-09-05 | 2018-07-17 | 西安艾润物联网技术服务有限责任公司 | Information presentation system and method |
-
2018
- 2018-12-21 CN CN201811573293.7A patent/CN109741629A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101169873A (en) * | 2007-11-26 | 2008-04-30 | 电子科技大学 | Abnormal driving intelligent checking system and checking method |
| CN102428504A (en) * | 2009-05-20 | 2012-04-25 | 欧宝士株式会社 | Driving assistance method and device |
| WO2014109973A1 (en) * | 2013-01-10 | 2014-07-17 | International Business Machines Corporation | Automatic driver modeling for integration of human-controlled vehicles into an autonomous vehicle network |
| CN103198685A (en) * | 2013-03-15 | 2013-07-10 | Tcl集团股份有限公司 | Method and system for achieving driving safety early warning |
| CN104090886A (en) * | 2013-12-09 | 2014-10-08 | 深圳市腾讯计算机系统有限公司 | Method and device for constructing real-time portrayal of user |
| CN104732785A (en) * | 2015-01-09 | 2015-06-24 | 杭州好好开车科技有限公司 | Driving behavior analyzing and reminding method and system |
| CN108289108A (en) * | 2016-09-05 | 2018-07-17 | 西安艾润物联网技术服务有限责任公司 | Information presentation system and method |
| CN106530828A (en) * | 2016-12-07 | 2017-03-22 | 深圳市元征科技股份有限公司 | Vehicle driving state early-warning method and device |
| CN107066616A (en) * | 2017-05-09 | 2017-08-18 | 北京京东金融科技控股有限公司 | Method, device and electronic equipment for account processing |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110766826A (en) * | 2019-09-10 | 2020-02-07 | 中国平安财产保险股份有限公司 | Driving behavior analysis method |
| CN111210620A (en) * | 2019-12-19 | 2020-05-29 | 广州航天海特系统工程有限公司 | Method, device and equipment for generating driver portrait and storage medium |
| WO2022105111A1 (en) * | 2020-11-17 | 2022-05-27 | 平安科技（深圳）有限公司 | Regional profile generation method and apparatus, computer device, and storage medium |
| CN112598905A (en) * | 2020-12-14 | 2021-04-02 | 苏州智能交通信息科技股份有限公司 | Passenger flow dynamic analysis early warning method, system and storage medium |
| CN112598905B (en) * | 2020-12-14 | 2021-10-26 | 苏州智能交通信息科技股份有限公司 | Passenger flow dynamic analysis early warning method, system and storage medium |
| CN114694412A (en) * | 2020-12-25 | 2022-07-01 | 腾讯科技（深圳）有限公司 | Vehicle safety management control method, device and system and electronic equipment |
| CN115497295A (en) * | 2022-09-21 | 2022-12-20 | 联通智网科技股份有限公司 | Safety early warning method and device |
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