CN105139648A

CN105139648A - Driving habit data generation method and system

Info

Publication number: CN105139648A
Application number: CN201510490612.8A
Authority: CN
Inventors: 罗松
Original assignee: Beijing Qihoo Technology Co Ltd; Qizhi Software Beijing Co Ltd
Current assignee: Beijing Qihoo Technology Co Ltd
Priority date: 2015-08-11
Filing date: 2015-08-11
Publication date: 2015-12-09
Anticipated expiration: 2035-08-11
Also published as: CN105139648B

Abstract

The invention provides a driving habit data generation method and system, and the method comprises the following steps: S1, collecting the driving behavior data of a user through a first remote interface, wherein the driving behavior data comprises a driving time speed and a driving acceleration value; S2, carrying out the statistics of the driving behavior data of at least one user in a time period, and generating the driving habit data of the driver according to the driving behavior data; S3, transmitting the driving habit data of the driver through a second remote interface. Through the statistics and analysis of the driving behavior data of the user, the method analyzes the driving habit data of the user according to the travelled distance, the driving track, the driving time, the driving speed, the acceleration speed and direction, and carries out the analysis, prediction and feedback of the risk degree corresponding to the driving habit data.

Description

Driving habits data creation method and system

Technical field

The present invention relates to driving data field, specifically, the present invention relates to a kind of driving habits data creation method, and relate to a kind of driving habits data generation system.

Background technology

Along with the income of residents increases, private car has more and more entered in the middle of family, it is reported 2014 the end of the year domestic private car have 1.05 hundred million, account for 90.16% of small-sized passenger car, its middle-size and small-size passenger car has 1.17 hundred million, by up-to-date census result, present Chinese population reaches 13.6 hundred million, and this just means in 1000 people, has 86 people to have car, if calculated by a family family 3 people, so every one hundred houses family has 25 private cars; So huge automobile pollution, causes the generation of traffic hazard more and more frequent, also causes a large amount of casualties and property loss simultaneously; And insurance company is also because of a large amount of traffic hazards, pay a large amount of properties, current insurance company can, according to the car insurance Claims Resolution situation of the previous year, consider to increase vehicle insurance or reduce next year.

Number of patent application is 201410269315.6, and patent name is that based on the driving habits information of traffic safety, such as lane change does not play lamp based in the long-distance monitoring method of traffic safety and the open file of system; Too near with car, do not keep a safe distance; Compacting line; Rush amber light; Fatigue driving etc., and these Information Statistics are reported to cloud server, and cloud server (such as fleet management center etc.) can be monitored the dangerous driving behavior of user and collect evidence, and then monitor and managment is carried out to user's driving behavior, be intended to improve drive safety and improve vehicle management dirigibility; Based on the long-distance monitoring method of traffic safety, should be analyze dangerous driving behavior based on monitoring driving custom, for traffic safety, have certain meaning, but this data, directly and exactly can not reflect the safety of driving behavior.

Summary of the invention

Object of the present invention is intended at least solve one of above-mentioned technological deficiency, particularly the driving behavior data of automobile side gathered and understand the driving habits of user, the hazard level of the driving habits numerical analysis user then utilizing these to generate, and then obtain the anticipation result and insured value etc. for user's driving behavior.

The invention provides a kind of driving habits data creation method, comprise the steps:

Step S1, gathers the driving behavior data of user by the first remote interface, described driving behavior data comprise driving speed per hour and travel acceleration size;

Step S2, adds up the described driving behavior data in a period of time of at least one user, according to the driving habits data of this user of this driving behavior data genaration;

Step S3, transmits the driving habits data of described user by the second remote interface.

In the present invention, the data of each user will be collected on the one hand by cloud server, then can exchange data with insurance company server on the other hand, by gathering the driving behavior data of automobile side and understanding the driving habits of described user, the insured value of the driving habits numerical analysis user then utilizing these to generate, finally can provide reference data to insurance company.

In reference data table, in every bar record of each user, can carry out classification analysis by different threshold values, these threshold values can carry out self-defined setting according to the actual conditions of user; As when speed per hour of driving a vehicle is more than 100Km/h, is defined as and runs at high speed; When acceleration magnitude is higher than certain numerical value, is defined as and brings to a halt; If there is this record exceeding threshold values more than 60% in driving behavior data, so, represent that the driving habits of this user is bad, driving habits data just can generate following form and embody: type-custom emergency brake; Emergency brake probability-60%; Risk factor-60%; And for example, if many numbers according to the show the long-term speed per hour of this user be not less than 120Km/h, so, the driving habits data of this user just can generate following form and embody: type-custom scorch; High speed probability-60%; Risk factor-60%.

Because according to the distance travelled that user drives a car, the data such as running time and driving speed per hour, the frequent degree that user drives can be analyzed, drive the factor that the drive speed etc. continuing duration and user habit affects driving safety, even, can judge whether user exists fatigue driving further, the probability of fatigue driving has much, whether driving condition whether stable and driving procedure middle rolling car speed per hour the factor such as controls in normal range, on this basis, by judging described automobile acceleration magnitude in the process of moving, whether normally speed change in user's driving behavior can also be analyzed again further, and then reflect the driving habits of user, the driving safety degree based on user's driving behavior can be reflected comparatively all sidedly.

The such scheme that the present invention proposes, carries out statistics and analysis by the driving behavior data for described automobile, and goes out the driving habits based on driving behavior data according to factor analyses such as distance travelled, running time, driving speed per hour and acceleration magnitude.

The aspect that the present invention adds and advantage will part provide in the following description, and these will become obvious from the following description, or be recognized by practice of the present invention.

Further improvement of the present invention is, also comprise step S4, described step S4 obtains the casualty data of described user by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, difference between the hazard level of more described casualty data and the hazard level of described driving habits data, and send by described first remote interface and/or the second remote interface the warning information comprising this difference.

Further improvement of the present invention is, hazard level in described driving habits data is by the described driving behavior data analysis to user, and then calculates the prediction hazard level of the driving habits data for reflecting described user being quantified as numerical value.

Further improvement of the present invention is, the hazard level of described casualty data is by analyzing the casualty data of user, and then calculates the actual danger degree of the casualty data for reflecting described user being quantified as numerical value.

In the present invention, the hazard level of described driving habits data refers to comparing according to driving habits data and threshold values, and then obtains the hazard level of the prediction exceeding threshold values, also claims prediction hazard level; With the prediction hazard level of described user's driving behavior unlike, the hazard level of described casualty data is that the accident number of times of the factors such as distance travelled, running time, automobile speed per hour and acceleration magnitude according to statistical study above and reality carries out analyzing out and relation between user's driving behavior of obtaining and driving safety, and the hazard level of this casualty data also claims actual danger degree; Relation between this user's driving behavior and driving safety is very targetedly and has uniqueness, this specific aim relation referred between this user's driving behavior and driving safety is user for each automobile and analyzes out, being another the very important index except the hazard level of the described driving habits data by calculating, is the real standard reflecting generation vehicle insurance settling fee.

The present invention, according to the difference between prediction hazard level and actual danger degree, judges whether the actual driving behavior of user is dangerous; Such as, under some too fast vehicle speed condition, difference between actual danger degree and prediction hazard level is less, if the user of a certain automobile never collides, so the quantized values of its actual hazard level is less, now, do not mean that the driving behavior hazard level of this user is high; And for certain some user in slow driving speed per hour situation, may owing to accelerating the too quickly or reason such as too quickly of turning round, the i.e. reason such as acceleration magnitude or acceleration exception, actual generation can traffic hazard, in this case, the difference between described prediction hazard level and actual danger degree may be larger etc.

Preferably, the hazard level of described driving habits data and the hazard level of casualty data are all given a mark and/or graded, be convenient to management, form systematic data, this systematic data can also feed back in the first remote interface and/or the second remote interface, and then provide comprehensive and accurately actual reference data for follow-up operation.

As according to as described in frequent degree corresponding to the driving behavior data of automobile, drive and continue duration, data such as driving speed per hour and acceleration magnitude etc., the frequent degree corresponding according to user's driving behavior of described automobile, drive and continue duration, driving speed per hour, the relation between data and the casualty data of this automobile such as acceleration magnitude, the frequent degree corresponding according to user's driving behavior of described automobile, drive and continue duration, driving speed per hour, relation between the data such as acceleration magnitude and prediction hazard level, and the frequent degree corresponding according to user's driving behavior of described automobile, drive and continue duration, driving speed per hour, relation between the data such as acceleration magnitude and actual danger degree is carried out having system and is comprehensively given a mark and/or grade, such data statistics, integrate and analyze, can hazard level corresponding to extraordinary embodiment driving behavior data, for user's driving behavior anticipation and result feedback provide very comprehensively and accurately data basis.

Even, can also for various data recited above and analysis, be further divided into fatigue driving classification, furious driving is classified, promptly turn round or turn to classification and other bad steering behaviour classification etc., various data are classified according to bad steering behavior, is convenient to the comparing between same classification and different classification and screening.

The present invention is according to the difference between prediction hazard level and actual danger degree, give corresponding information feed back, it is how many for being convenient to the difference analyzed between prediction hazard level and actual danger degree, can also analyze this difference is further where produce, why can there is this difference, like this, the recessive relation between driving behavior in some cases and the actual traffic hazard produced can just be analyzed.

Further improvement of the present invention is, also comprise step S4 ', described step S4 ' obtains the casualty data of described user by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, then obtain the hazard level of described casualty data and the hazard level of described driving habits data by the 3rd remote interface.That is, the present invention not necessarily arranges difference comparsion, but casualty data and described driving habits data are included the hazard level being quantified as numerical value is transferred to the 3rd remote interface, as third party's reference data.

Further improvement of the present invention is, is also analyzed the driving behavior data of described user by the first remote interface, and then produces the first cost information.

Described first cost information is the driving behavior data that insurance company analyzes described automobile, and then calculate vehicle insurance expense, the calculating of described first cost information, comprehensive test can be carried out in conjunction with the price of described automobile, model, vehicle insurance insurance expenses in the past, vehicle insurance settling fee in the past and the factor such as user's driving behavior analyzing gained, and then obtain the vehicle insurance expense that an insurance company provides; Further, described first remote interface can according to analyzing the user's driving behavior obtained, calculate and feed back the prediction hazard level of the driving behavior data of described automobile, and carry out giving a mark and/or grading to this prediction hazard level, be convenient to management, form systematic data, for user's driving behavior anticipation of other automobiles and result feedback provide reference data.

Further improvement of the present invention is, is also analyzed the casualty data of described user by the second remote interface, and then produces the second cost information.

Described second cost information is according to driving behavior data, and the actual vehicle insurance settling fee produced, the driving result of this reality is the standard of real generation vehicle insurance settling fee, it is a very important index, the collision frequency occurred by described automobile in the second remote interface statistics set time section and then generation the second cost information, can reflect in the set time section of specifying well, the collision frequency that described automobile occurs, in conjunction with the distance travelled of institute's statistical study above, running time, automobile speed per hour and acceleration magnitude, the frequent degree of the driving of user in set time section can be analyzed completely, drive and continue duration, relation between the collision frequency that driving speed per hour and acceleration magnitude and automobile occur, this relation can pass through trend map, various forms such as number chart or scatter diagram etc. feeds back, for insurance company and/or background monitoring center, this is one and very has practical significance, for user, user also can be allowed more intuitively to arrive relation between the driving behavior of oneself and car crass, and then instruct user security to drive, there is very important social effect, the setting of this set time section, can carry out self-defined setting according to actual needs, such as, be set to 1 year, one month or certain frequently go out time period of car can.

The present invention can also comprise the step arranging trigger alarm, described step S1 can reflect the biological information of user from the side, as the distance travelled according to user's driving behavior, the data such as running time and automobile speed per hour judge whether the whether qualified and user of the alertness of user has entered the category of fatigue driving, if if alertness has entered the category of fatigue driving lower than criterion of acceptability or user, then can be sent warning message by the step triggering early warning to car terminals, and then as far as possible before mishap occurs, effective early warning can be obtained, and then avoid the generation of mishap as far as possible.

On this basis, if the acceleration magnitude analyzed increases to suddenly some threshold values, also represent user's driving behavior and add suddenly its hazard level, this time, also warning message can be sent by the step triggering early warning to car terminals, call user's attention traffic safety; How threshold values as this acceleration magnitude arranges and is arranged within the scope of what, then can arrange according to the actual conditions of user, also the unexpected acceleration information such as can to collide by analyzing user, the unexpected data according to this reality are arranged as with reference to data in the past.

Further improvement of the present invention is, calculates the expense difference between described first cost information and the second cost information according to the first cost information and the second cost information.

The present invention is on the basis calculating described first cost information and the second cost information, computational costs difference can be carried out further according to the first cost information and the second cost information, and then the expense situation that feedback is actual, in this, as a dynamic data analysis, the reference of a good prediction expense can be provided to user; Further, along with continuous increase and the correction of data volume, the expense difference between described first cost information and the second cost information will be more and more less, make the reference value of the first cost information increasing, and progressively trend towards the expense of actual generation.

Further improvement of the present invention is, in described step S1, described driving behavior data also comprise wheelpath and acceleration direction.

Further improvement of the present invention is, also comprise step S5, described step S5 to have an accident the wheelpath of place time zone, driving speed per hour, acceleration magnitude and acceleration direction according to described automobile, the relation between the casualty data of wheelpath described in real-time analysis, driving speed per hour, acceleration magnitude and acceleration direction and described user.

Further improvement of the present invention is, relation between the casualty data of described wheelpath, driving speed per hour, acceleration magnitude and acceleration direction and described user is back to the first remote interface and/or the second remote interface, and in this, as analyzing a corrected parameter of described driving habits data.

In step S1 of the present invention, also record driving trace and the acceleration direction of automobile.By increasing the record of driving trace, from driving trace, more indexs that can affect driving safety can be analyzed; On this basis, described automobile acceleration magnitude in the process of moving and acceleration direction is judged by automobile speed per hour and driving trace.

The present invention is by decomposing automobile speed per hour and the driving trace of described automobile on a timeline, can analyze completely and judge described automobile acceleration magnitude in the process of moving and acceleration direction, these two data, represent the instantaneous velocity in vehicle traveling process and direction, the Fundamentals affecting driving safety, by increasing acceleration magnitude and the relation between acceleration direction and prediction hazard level, and increase acceleration magnitude and the relation between acceleration direction and actual danger degree, statistical can separate out the much potential traffic safety problem relevant to driving behavior.

The present invention by carrying out data statistics and analysis to driving behavior data, involved by statistics and analysis user driving behavior about distance travelled, running time, driving speed per hour, the data such as acceleration magnitude and acceleration direction, except analyzing the frequent degree of the driving of user in set time section, drive and continue duration, user's drive speed, user's driving fatigue degree, acceleration magnitude, outside the important parameters such as the collision frequency that acceleration direction and automobile occur, can also the urgency of Real-Time Monitoring user accelerate, anxious deceleration, zig zag, hypervelocity, start under clutch coupling higher load condition, engine idle runs, under engine low temperature, rotating speed is too high and long-time continue bad user's driving behavior such as brake, the relation between these bad user's driving behaviors and the actual collision frequency occurred of automobile can be analyzed, and then realize about the data sheet etc. of user's driving behavior, as to brake use habit, clutch coupling use habit, the analysis of driving style and bad steering custom etc. also provides journaling, and then break down at vehicle, as collision happens, user's furious driving, long-time continuous is driven, rotating speed is too high, suddenly accelerate, anxious slow down and stop the phenomenon such as flame-out time, carry out warning indicators with trigger alarm step.

The present invention is also provided with corrected parameter, to collide the distance travelled of place time zone, running time, acceleration magnitude and acceleration direction according to described automobile, relation between vehicle driving mileage, running time, acceleration magnitude and acceleration direction and this automobile described in real-time analysis collide, and then as being used for the corrected parameter of computational prediction hazard level.

Because must difference be there is between prediction hazard level and actual danger degree, this difference is according to user's driving behavior of each user, automobile actual state, driving the actual conditions such as the emergency processing power of road conditions and user can each difference, that is, described prediction hazard level can be distinguished according to the different of user to some extent from the difference between actual danger degree, cannot treat different things as the same, so, the prediction hazard level of the user's driving behavior corresponding to each user and actual danger degree how can be made to coincide as far as possible, an emphasis analyzed beyond doubt, but be also a difficult point simultaneously.

The present invention is by vehicle driving mileage described in real-time analysis, running time, driving speed per hour, acceleration magnitude and acceleration direction and this automobile is actual have an accident between relation, and then by described vehicle driving mileage, running time, acceleration magnitude and acceleration direction and this automobile is actual have an accident between relation constantly revise as the prediction hazard level of a corrected parameter for the driving behavior data of this automobile, As time goes on the increase of data volume, can make prediction hazard level and actual danger degree more and more identical.

The present invention also provides a kind of driving habits data generation system, comprises as lower module:

Driving behavior data acquisition module, gathers the driving behavior data of user by the first remote interface, described driving behavior data comprise driving speed per hour and travel acceleration size;

Driving habits data generation module, adds up the described driving behavior data in a period of time of at least one user, according to the driving habits data of this user of this driving behavior data genaration;

Data transmission module, transmits the driving habits data of described user by the second remote interface.

Further improvement of the present invention is, also comprise the first data and quantize feedback module, described first data quantize feedback module obtains described user casualty data by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, difference between the hazard level of more described casualty data and the hazard level of described driving habits data, and send by described first remote interface and/or the second remote interface the warning information comprising this difference.

Further improvement of the present invention is, also comprise the second data and quantize feedback module, described second data quantize feedback module obtains described user casualty data by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, then obtain the hazard level of described casualty data and the hazard level of described driving habits data by the 3rd remote interface.That is, the present invention not necessarily arranges difference comparsion, but quantizes feedback module by the second data and casualty data and described driving habits data are included the hazard level being quantified as numerical value be transferred to the 3rd remote interface, as third party's reference data.

The present invention can also comprise and arranges trigger alarm module, described driving behavior data acquisition module can reflect the biological information of user from the side, as the distance travelled according to user's driving behavior, the data such as running time and automobile speed per hour judge whether the whether qualified and user of the alertness of user has entered the category of fatigue driving, if if alertness has entered the category of fatigue driving lower than criterion of acceptability or user, then can send warning message to car terminals by triggering warning module, and then as far as possible before mishap occurs, effective early warning can be obtained, and then avoid the generation of mishap as far as possible.

On this basis, if the acceleration magnitude analyzed increases to suddenly some threshold values, also represent user's driving behavior and add suddenly its hazard level, this time, also warning message can be sent to car terminals, call user's attention traffic safety by triggering warning module; How threshold values as this acceleration magnitude arranges and is arranged within the scope of what, then can arrange according to the actual conditions of user, also the unexpected acceleration information such as can to collide by analyzing user, the unexpected data according to this reality are arranged as with reference to data in the past.

Further improvement of the present invention is, in described driving behavior data acquisition module, described driving behavior data also comprise wheelpath and acceleration direction.

Further improvement of the present invention is, also comprise qualitative analysis module, described qualitative analysis module to have an accident the wheelpath of place time zone, driving speed per hour, acceleration magnitude and acceleration direction according to described automobile, the relation between the casualty data of wheelpath described in real-time analysis, driving speed per hour, acceleration magnitude and acceleration direction and described user.

Further improvement of the present invention is, relation between the casualty data of described wheelpath, driving speed per hour, acceleration magnitude and acceleration direction and described user is back to the first remote interface and/or the second remote interface, and in this, as analyzing a corrected parameter of described driving habits data

In driving behavior data acquisition module of the present invention, also record driving trace and the acceleration direction of automobile.By increasing the record of driving trace, from driving trace, more indexs that can affect driving safety can be analyzed; On this basis, described automobile acceleration magnitude in the process of moving and acceleration direction is judged by automobile speed per hour and driving trace.

Accompanying drawing explanation

The present invention above-mentioned and/or additional aspect and advantage will become obvious and easy understand from the following description of the accompanying drawings of embodiments, wherein:

Fig. 1 is the workflow schematic diagram of the embodiment of the present invention 1;

Fig. 2 is the system architecture schematic diagram of the embodiment of the present invention 2.

Embodiment

Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.

Embodiment 1:

Refer to Fig. 1, this example provides a kind of driving habits data creation method, comprises the steps:

In this example, the data of each user will be collected on the one hand by cloud server, then can exchange data with insurance company server on the other hand, by gathering the driving behavior data of automobile side and understanding the driving habits of described user, the insured value of the driving habits numerical analysis user then utilizing these to generate, finally can provide reference data to insurance company.

The such scheme that this example proposes, carries out statistics and analysis by the driving behavior data for described automobile, and goes out the driving habits based on driving behavior data according to factor analyses such as distance travelled, running time, driving speed per hour and acceleration magnitude.

The aspect that this example is additional and advantage will part provide in the following description, and these will become obvious from the following description, or be recognized by the practice of this example.

Refer to Fig. 1, also comprise step S4, described step S4 obtains the casualty data of described user by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, difference between the hazard level of more described casualty data and the hazard level of described driving habits data, and send by described first remote interface and/or the second remote interface the warning information comprising this difference.

Those skilled in the art of the present technique are appreciated that, hazard level in described driving habits data is by the described driving behavior data analysis to user, and then calculates the prediction hazard level of the driving habits data for reflecting described user being quantified as numerical value.

Those skilled in the art of the present technique are appreciated that the hazard level of described casualty data is by analyzing the casualty data of user, and then calculate the actual danger degree of the casualty data for reflecting described user being quantified as numerical value.

In this example, the hazard level of described driving habits data refers to comparing according to driving habits data and threshold values, and then obtains the hazard level of the prediction exceeding threshold values, also claims prediction hazard level; With the prediction hazard level of described user's driving behavior unlike, the hazard level of described casualty data is that the accident number of times of the factors such as distance travelled, running time, automobile speed per hour and acceleration magnitude according to statistical study above and reality carries out analyzing out and relation between user's driving behavior of obtaining and driving safety, and the hazard level of this casualty data also claims actual danger degree; Relation between this user's driving behavior and driving safety is very targetedly and has uniqueness, this specific aim relation referred between this user's driving behavior and driving safety is user for each automobile and analyzes out, being another the very important index except the hazard level of the described driving habits data by calculating, is the real standard reflecting generation vehicle insurance settling fee.

Those skilled in the art of the present technique are appreciated that according to the difference between prediction hazard level and actual danger degree, judge whether the actual driving behavior of user is dangerous; Such as, under some too fast vehicle speed condition, difference between actual danger degree and prediction hazard level is less, if the user of a certain automobile never collides, so the quantized values of its actual hazard level is less, now, do not mean that the driving behavior hazard level of this user is high; And for certain some user in slow driving speed per hour situation, may owing to accelerating the too quickly or reason such as too quickly of turning round, the i.e. reason such as acceleration magnitude or acceleration exception, actual generation can traffic hazard, in this case, the difference between described prediction hazard level and actual danger degree may be larger etc.

Those skilled in the art of the present technique are appreciated that, the hazard level of described driving habits data and the hazard level of casualty data are all given a mark and/or graded, be convenient to management, form systematic data, this systematic data can also feed back in the first remote interface and/or the second remote interface, and then provide comprehensive and accurately actual reference data for follow-up operation.

This example is according to the difference between prediction hazard level and actual danger degree, give corresponding information feed back, it is how many for being convenient to the difference analyzed between prediction hazard level and actual danger degree, can also analyze this difference is further where produce, why can there is this difference, like this, the recessive relation between driving behavior in some cases and the actual traffic hazard produced can just be analyzed.

Those skilled in the art of the present technique are appreciated that, step S4 is replaced with step S4 ', described step S4 ' obtains the casualty data of described user by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, then obtain the hazard level of described casualty data and the hazard level of described driving habits data by the 3rd remote interface.That is, this example not necessarily arranges difference comparsion, but casualty data and described driving habits data are included the hazard level being quantified as numerical value is transferred to the 3rd remote interface, as third party's reference data.

Those skilled in the art of the present technique are appreciated that the driving behavior data being analyzed described user by the first remote interface, and then produce the first cost information.

Those skilled in the art of the present technique are appreciated that the casualty data being analyzed described user by the second remote interface, and then produce the second cost information.

Those skilled in the art of the present technique are appreciated that, this example can also comprise the step arranging trigger alarm, described step S1 can reflect the biological information of user from the side, as the distance travelled according to user's driving behavior, the data such as running time and automobile speed per hour judge whether the whether qualified and user of the alertness of user has entered the category of fatigue driving, if if alertness has entered the category of fatigue driving lower than criterion of acceptability or user, then can be sent warning message by the step triggering early warning to car terminals, and then as far as possible before mishap occurs, effective early warning can be obtained, and then avoid the generation of mishap as far as possible.

Those skilled in the art of the present technique are appreciated that, the expense difference between described first cost information and the second cost information is calculated according to the first cost information and the second cost information, and then the expense situation that feedback is actual, in this, as a dynamic data analysis, the reference of a good prediction expense can be provided to user; Further, along with continuous increase and the correction of data volume, the expense difference between described first cost information and the second cost information will be more and more less, make the reference value of the first cost information increasing, and progressively trend towards the expense of actual generation.

Those skilled in the art of the present technique are appreciated that in described step S1, and described driving behavior data also comprise wheelpath and acceleration direction.

Refer to Fig. 1, this example also comprises step S5, described step S5 to have an accident the wheelpath of place time zone, driving speed per hour, acceleration magnitude and acceleration direction according to described automobile, the relation between the casualty data of wheelpath described in real-time analysis, driving speed per hour, acceleration magnitude and acceleration direction and described user.

Relation between the casualty data of described wheelpath, driving speed per hour, acceleration magnitude and acceleration direction and described user is back to the first remote interface and/or the second remote interface by this example, and in this, as analyzing a corrected parameter of described driving habits data.

In this routine described step S1, also record driving trace and the acceleration direction of automobile.By increasing the record of driving trace, from driving trace, more indexs that can affect driving safety can be analyzed; On this basis, described automobile acceleration magnitude in the process of moving and acceleration direction is judged by automobile speed per hour and driving trace.

This example is by decomposing automobile speed per hour and the driving trace of described automobile on a timeline, can analyze completely and judge described automobile acceleration magnitude in the process of moving and acceleration direction, these two data, represent the instantaneous velocity in vehicle traveling process and direction, the Fundamentals affecting driving safety, by increasing acceleration magnitude and the relation between acceleration direction and prediction hazard level, and increase acceleration magnitude and the relation between acceleration direction and actual danger degree, statistical can separate out the much potential traffic safety problem relevant to driving behavior.

This example by carrying out data statistics and analysis to driving behavior data, involved by statistics and analysis user driving behavior about distance travelled, running time, driving speed per hour, the data such as acceleration magnitude and acceleration direction, except analyzing the frequent degree of the driving of user in set time section, drive and continue duration, user's drive speed, user's driving fatigue degree, acceleration magnitude, outside the important parameters such as the collision frequency that acceleration direction and automobile occur, can also the urgency of Real-Time Monitoring user accelerate, anxious deceleration, zig zag, hypervelocity, start under clutch coupling higher load condition, engine idle runs, under engine low temperature, rotating speed is too high and long-time continue bad user's driving behavior such as brake, the relation between these bad user's driving behaviors and the actual collision frequency occurred of automobile can be analyzed, and then realize about the data sheet etc. of user's driving behavior, as to brake use habit, clutch coupling use habit, the analysis of driving style and bad steering custom etc. also provides journaling, and then break down at vehicle, as collision happens, user's furious driving, long-time continuous is driven, rotating speed is too high, suddenly accelerate, anxious slow down and stop the phenomenon such as flame-out time, carry out warning indicators with trigger alarm step.

Those skilled in the art of the present technique are appreciated that, this example is also provided with corrected parameter, to collide the distance travelled of place time zone, running time, acceleration magnitude and acceleration direction according to described automobile, relation between vehicle driving mileage, running time, acceleration magnitude and acceleration direction and this automobile described in real-time analysis collide, and then as being used for the corrected parameter of computational prediction hazard level.

This example is by vehicle driving mileage described in real-time analysis, running time, driving speed per hour, acceleration magnitude and acceleration direction and this automobile is actual have an accident between relation, and then by described vehicle driving mileage, running time, acceleration magnitude and acceleration direction and this automobile is actual have an accident between relation constantly revise as the prediction hazard level of a corrected parameter for the driving behavior data of this automobile, As time goes on the increase of data volume, can make prediction hazard level and actual danger degree more and more identical.

Embodiment 2:

Refer to Fig. 2, this example also provides a kind of driving habits data generation system, comprises as lower module:

Refer to Fig. 2, this example also comprises the first data and quantizes feedback module, described first data quantize feedback module obtains described user casualty data by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, difference between the hazard level of more described casualty data and the hazard level of described driving habits data, and send by described first remote interface and/or the second remote interface the warning information comprising this difference.

Those skilled in the art of the present technique are appreciated that, this example can also quantize feedback module by the second data and replace described first data quantification feedback module, described second data quantize feedback module obtains described user casualty data by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, then obtain the hazard level of described casualty data and the hazard level of described driving habits data by the 3rd remote interface.That is, this example not necessarily arranges difference comparsion, but quantizes feedback module by the second data and casualty data and described driving habits data are included the hazard level being quantified as numerical value be transferred to the 3rd remote interface, as third party's reference data.

Those skilled in the art of the present technique are appreciated that, this example can also comprise and arranges trigger alarm module, described driving behavior data acquisition module can reflect the biological information of user from the side, as the distance travelled according to user's driving behavior, the data such as running time and automobile speed per hour judge whether the whether qualified and user of the alertness of user has entered the category of fatigue driving, if if alertness has entered the category of fatigue driving lower than criterion of acceptability or user, then can send warning message to car terminals by triggering warning module, and then as far as possible before mishap occurs, effective early warning can be obtained, and then avoid the generation of mishap as far as possible.

In this routine described driving behavior data acquisition module, described driving behavior data also comprise wheelpath and acceleration direction.

Refer to Fig. 2, this example also comprises qualitative analysis module, described qualitative analysis module to have an accident the wheelpath of place time zone, driving speed per hour, acceleration magnitude and acceleration direction according to described automobile, the relation between the casualty data of wheelpath described in real-time analysis, driving speed per hour, acceleration magnitude and acceleration direction and described user.

Relation between the casualty data of described wheelpath, driving speed per hour, acceleration magnitude and acceleration direction and described user is back to the first remote interface and/or the second remote interface by this example, and in this, as analyzing a corrected parameter of described driving habits data

In this routine described driving behavior data acquisition module, also record driving trace and the acceleration direction of automobile.By increasing the record of driving trace, from driving trace, more indexs that can affect driving safety can be analyzed; On this basis, described automobile acceleration magnitude in the process of moving and acceleration direction is judged by automobile speed per hour and driving trace.

The above is only some embodiments of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.

Claims

1. a driving habits data creation method, is characterized in that, comprises the steps:

2. driving habits data creation method according to claim 1, it is characterized in that, also comprise step S4, described step S4 obtains the casualty data of described user by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, difference between the hazard level of more described casualty data and the hazard level of described driving habits data, and send by described first remote interface and/or the second remote interface the warning information comprising this difference.

3. driving habits data creation method according to claim 1, it is characterized in that, also comprise step S4 ', described step S4 ' obtains the casualty data of described user by described second remote interface, described casualty data and described driving habits data include the hazard level being quantified as numerical value, then obtain the hazard level of described casualty data and the hazard level of described driving habits data by the 3rd remote interface.

4. the driving habits data creation method according to Claims 2 or 3, it is characterized in that, hazard level in described driving habits data is by the described driving behavior data analysis to user, and then calculates the prediction hazard level of the driving habits data for reflecting described user being quantified as numerical value.

5. the driving habits data creation method according to Claims 2 or 3, it is characterized in that, the hazard level of described casualty data is by analyzing the casualty data of user, and then calculates the actual danger degree of the casualty data for reflecting described user being quantified as numerical value.

6. the driving habits data creation method according to claims 1 to 3 any one, is characterized in that, is also analyzed the driving behavior data of described user by the first remote interface, and then produces the first cost information.

7. driving habits data creation method according to claim 6, is characterized in that, is also analyzed the casualty data of described user by the second remote interface, and then produces the second cost information.

8. driving habits data creation method according to claim 7, is characterized in that, calculates the expense difference between described first cost information and the second cost information according to the first cost information and the second cost information.

9. the driving habits data creation method according to claims 1 to 3 any one, is characterized in that, in described step S1, described driving behavior data also comprise wheelpath and acceleration direction.

10. a driving habits data generation system, is characterized in that, comprises as lower module: