CN109754595A - Appraisal procedure, device and the interface equipment of vehicle risk - Google Patents
Appraisal procedure, device and the interface equipment of vehicle risk Download PDFInfo
- Publication number
- CN109754595A CN109754595A CN201711059783.0A CN201711059783A CN109754595A CN 109754595 A CN109754595 A CN 109754595A CN 201711059783 A CN201711059783 A CN 201711059783A CN 109754595 A CN109754595 A CN 109754595A
- Authority
- CN
- China
- Prior art keywords
- risk
- vehicle
- driving
- data
- feature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
This application discloses a kind of appraisal procedure of vehicle risk, device and interface equipments.Wherein, this method comprises: obtaining at least a kind of vehicle data of vehicle, according at least a kind of vehicle data, the risk for obtaining existing at least one risk of driving when driving vehicle determines the risk assessment value of vehicle based on the risk of at least one risk of driving.Present application addresses the prior arts to the technical problem of the assessment inaccuracy of vehicle risk.
Description
Technical field
This application involves risk assessment fields, in particular to a kind of appraisal procedure of vehicle risk, device and interface
Equipment.
Background technique
The problem of assessment of vehicle drive risk is traffic department's institute's critical concern, especially for two visitor one danger (refer to from
Thing tourism hired car, three classes with go to work line car and peril of transportation chemicals, fireworks and firecrackers, the road of industrial explosive materials it is dedicated
Vehicle) vehicle, once accident occurs for vehicle, will generate it is immeasurable the person and property loss.Therefore, vehicle is driven
Sailing, which nearly carries out effective assessment, not only may insure the safety of driver, also reduce economic loss.In addition, to vehicle
Driving risk carry out assessment and also help insurance company to price fixing, and then driver is encouraged by price fixing
Carry out safe driving.
Currently, the positioning device with traveling writing function is equipped with mostly on the vehicle of two visitors, one danger, for example, GPS is fixed
Position device, the prior art mainly use the data of vehicle GPS to calculate the driving behavior of vehicle, for example, the traveling speed of vehicle
Degree, acceleration etc. assess the driving risk of vehicle with this, to ensure the driving safety of driver.
However, the GPS data of vehicle and insurance data be it is mutually isolated, i.e., in the prior art, driven to vehicle
When sailing is nearly assessed, usually according only to the GPS data of vehicle, or according only to insurance data.In addition, being driven to vehicle
When sailing is nearly assessed, it is also necessary to risks and assumptions are added, and the determination of risks and assumptions is mainly the subjective experience by expert,
Actual data supporting is had no, thereby resulting in assessment to vehicle risk, there may be the problems of inaccuracy.
For the above-mentioned prior art to the problem of the assessment inaccuracy of vehicle risk, effective solution side is not yet proposed at present
Case.
Summary of the invention
The embodiment of the present application provides appraisal procedure, device and the interface equipment of a kind of vehicle risk, existing at least to solve
There is technology to the technical problem of the assessment inaccuracy of vehicle risk.
According to the one aspect of the embodiment of the present application, a kind of appraisal procedure of vehicle risk is provided, comprising: obtain vehicle
At least a kind of vehicle data；According at least a kind of vehicle data, obtains existing at least one when driving vehicle and drive risk
Risk；Based on the risk of at least one risk of driving, the risk assessment value of vehicle is determined.
According to the another aspect of the embodiment of the present application, a kind of interface equipment is additionally provided, is the appraisal procedure of vehicle risk
Api interface service is provided.
According to the one aspect of the embodiment of the present application, a kind of assessment device of vehicle risk is additionally provided, comprising: first obtains
Modulus block, for obtaining at least a kind of vehicle data of vehicle；Second obtains module, is used for according at least a kind of vehicle data,
Obtain the risk of existing at least one risk of driving when driving vehicle；Determining module, for being driven wind based at least one
The risk of danger, determines the risk assessment value of vehicle.
According to the one aspect of the embodiment of the present application, a kind of storage medium is additionally provided, which includes storage
Program, wherein equipment where control storage medium executes the appraisal procedure of vehicle risk in program operation.
According to the one aspect of the embodiment of the present application, a kind of processor is additionally provided, which is used to run program,
In, the appraisal procedure of vehicle risk is executed when program is run.
According to the one aspect of the embodiment of the present application, a kind of terminal is additionally provided, comprising: data acquisition unit, for obtaining
The data of at least a kind of vehicle of pick-up；Processor, processor run program, wherein for adopting from data when program is run
The data for collecting unit output execute the appraisal procedure of vehicle risk.
According to the one aspect of the embodiment of the present application, a kind of terminal is additionally provided, comprising: data acquisition unit, for obtaining
The data of at least a kind of vehicle of pick-up；Storage medium, for storing program, wherein program is at runtime for from data
The data of acquisition unit output execute the appraisal procedure of vehicle risk.
According to the one aspect of the embodiment of the present application, a kind of system is additionally provided, comprising: processor；And memory, with
Processor connection, for providing the instruction for handling following processing step for processor: obtaining at least a kind of vehicle data of vehicle；
According at least a kind of vehicle data, the risk of existing at least one risk of driving when driving vehicle is obtained；Based at least one
The risk for planting risk of driving, determines the risk assessment value of vehicle.
In the embodiment of the present application, in such a way that a variety of vehicle datas assess vehicle risk, by obtaining vehicle extremely
Few one kind vehicle data, and the wind of existing at least one risk of driving when driving vehicle is obtained according at least a kind of vehicle data
Dangerous degree, the risk finally based at least one risk of driving, determines the risk assessment value of vehicle, has reached accurately to vehicle wind
The purpose that danger is assessed guarantees driver's safe driving to realize, and reduces the technical effect that traffic accident occurs, into
And solves the prior art to the technical problem of the assessment inaccuracy of vehicle risk.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please are not constituted an undue limitation on the present application for explaining the application.In the accompanying drawings:
Fig. 1 (a) is the assessment system structural schematic diagram according to a kind of optional vehicle risk of the embodiment of the present application；
Fig. 1 (b) is one kind according to the embodiment of the present application optionally based on the interaction schematic diagram of vehicle assessment system；
Fig. 2 is the flow chart according to a kind of appraisal procedure of vehicle risk of the embodiment of the present application；
Fig. 3 is the flow chart according to a kind of appraisal procedure of optional vehicle risk of the embodiment of the present application；
Fig. 4 is the flow chart according to a kind of appraisal procedure of optional vehicle risk of the embodiment of the present application；
Fig. 5 is the structural schematic diagram according to a kind of assessment device of vehicle risk of the embodiment of the present application；And
Fig. 6 is the hardware block diagram according to a kind of terminal of the embodiment of the present application.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
The embodiment of the application a part, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, all should belong to the model of the application protection
It encloses.
It should be noted that the description and claims of this application and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to embodiments herein described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
Embodiment 1
According to the embodiment of the present application, a kind of appraisal procedure embodiment of vehicle risk is provided, it should be noted that this Shen
The appraisal procedure of the vehicle risk please proposed can get a variety of vehicle datas from a variety of data channels, and from multiple dimensions
Degree accurately assesses vehicle risk so as to reach to assess vehicle risk, and then ensures that driver pacifies
It is complete to drive.
In addition it is also necessary to which explanation, the appraisal procedure of vehicle risk provided herein can be widely used in wind
Dangerous evaluation areas, including but not limited to the assessment of vehicle risk.Specifically, driver is during driving vehicle, vehicle
GPS positioning module upload the GPS data of vehicle in real time into the database of vehicle risk assessment system, meanwhile, vehicle risk
Data, the vehicle risks such as the data of car insurance, the electronic road form data of vehicle is also stored in the database of assessment system to comment
Estimate system and export the scoring of vehicle risk using the data in database as the input of off-line model, and then according to vehicle risk
Scoring to determine the driving risk of vehicle, and remind drive vehicle driver's safe driving.For example, being determined according to scoring
The duration of a length of fatigue driving when the driving of driver, then remind driver to take a good rest to ensure driving safety.
As shown in the above, isolated multiple data sources are fused in the same database by the application, and according to number
Comprehensive assessment is carried out to vehicle risk according to the data in library, to achieve the purpose that carry out accurate evaluation to vehicle risk.Its
In, Fig. 1 (a) shows a kind of assessment system structural representation of the vehicle risk of optional appraisal procedure for executing vehicle risk
Figure, as shown in Fig. 1 (a), assessment system includes: offline database and off-line model.Wherein, the data source master of offline database
It to include following three: the collected vehicle GPS track data of vehicle GPS institute, car insurance data, vehicle electric road odd number
According to.In addition, creation has off-line model in vehicle risk assessment system, the assessment knot of vehicle risk can be obtained by the off-line model
Fruit.Wherein, the data in offline database are the input data of the off-line mode, and off-line model is from driving behavior risk and drives
Two aspects of route risk are sailed to assess the risk of vehicle.Firstly, off-line model is according to the data structure in offline database
The driving behavior factor and the drive route factor are built, and extracts the key risk factor in the above two factor respectively and is then based on
The key risk factor being drawn into calculates separately the driving behavior value-at-risk and drive route value-at-risk of driver, finally, root
The assessed value of vehicle risk is determined according to driving behavior value-at-risk and drive route value-at-risk, and the assessed value of vehicle risk is stored
Into online database.Online database is connect by API service interface with external server, and server passes through api interface
The risk assessment value of vehicle can be got, and reminds driver's safe driving according to risk assessment value.
In an alternative embodiment, Fig. 1 (b) shows a kind of interaction signal optionally based on vehicle assessment system
Figure.In Fig. 1 (b), at least one external data base store respectively vehicle GPS track data, car insurance record data and
Vehicle electric road forms data.Wherein, interacting between external data base, risk evaluating system, online database and terminal
Journey is as follows:
Collected vehicle GPS track data, car insurance are recorded data by step S21, at least one external data base
And the vehicle datas such as vehicle electric road forms data are sent in the offline database in risk evaluating system；
Step S23, offline database store vehicle GPS track data, car insurance record data and vehicle electric road
Forms data, risk evaluating system obtain above-mentioned three kinds of data as the input of off-line model, and by the processing of off-line model
Risk assessment value；
Step S25, risk evaluating system store risk assessment value into online database；
Step S27, service terminal obtain the risk assessment value of the vehicle in online database by taking API service interface,
And remind driver's safe driving.
Under above-mentioned running environment, this application provides the appraisal procedures of vehicle risk as shown in Figure 2.Fig. 2 is basis
The flow chart of the appraisal procedure of the vehicle risk of the embodiment of the present application one.Wherein, the appraisal procedure of vehicle risk specifically include as
Lower step:
Step S202 obtains at least a kind of vehicle data of vehicle.
It should be noted that the vehicle data of vehicle includes at least one following: positioning of the vehicle on Crane Rail is beaten
The electronic road form data of point data and vehicle, wherein electronic road form data can include but is not limited to the starting point of vehicle driving, end
The information such as quantity, weight, type, the volume of the transported cargo of point, vehicle.
In an alternative embodiment, GPS positioning module is installed, GPS positioning module is every preset time on vehicle
The collected data of GPS positioning module institute are uploaded to shown in Fig. 1 (a) by (for example, 3-10 seconds) or the every traveling pre-determined distance of vehicle
Offline database in.Wherein, it is the collected number of GPS positioning module institute that data are got in positioning of the vehicle on Crane Rail, which ready,
According to the data include but is not limited to longitude, latitude, the deflection of vehicle when acquiring data.In addition, being beaten according to continuous two positioning
The travel speed of vehicle can be calculated in the longitude and latitude of point data and time difference.
It should be noted that the electronic road form data due to vehicle are not real-time change, for example, vehicle transport complete one
The secondary electronic road form data for obtaining vehicle just will be updated once.Therefore, by electronic road form data be uploaded to offline database when
Between interval should be greater than the uplink time interval that data are got in positioning of the vehicle on Crane Rail ready, for example, electronic road form data can
It uploads weekly primary.
In an alternative embodiment, external data base (database i.e. except the assessment system of vehicle risk) packet
Include wheelpath database, car insurance database of record, electronic road form database, wherein the GPS positioning module of all vehicles
Data are got ready every the positioning by vehicle on Crane Rail in 1 second to be uploaded in wheelpath database, and wheelpath data
Data are got ready every the positioning by all vehicles in 10 seconds and are uploaded in offline database in library.Equally, electronic road form database purchase has
The electronic road form data of all vehicles, car insurance database of record are stored with the insurance data of all vehicles, electronic road form number
Batch data is imported into offline database every other week according to library and car insurance database of record.
It should be noted that the assessment system of vehicle risk can obtain a variety of vehicle datas, to a variety of by step S202
Data, which carry out integrated treatment, can achieve the purpose that accurate evaluation is carried out to the risk of vehicle.
Step S204 obtains existing at least one risk of driving when driving vehicle according at least a kind of vehicle data
Risk.
It should be noted that risk of driving includes at least: drive behaviorist risk and path risk of driving, wherein be used for table
Sign drive behaviorist risk behavioural characteristic of driving include at least it is one of following: feature of risk based on speed, based on acceleration
Feature of risk, the feature of risk based on turning and the feature of risk based on fatigue driving；For characterizing the road for path risk of driving
Diameter feature includes at least: feature of risk based on drive time, the feature of risk based on vehicle carrying, based on the wind of path length
Dangerous feature and feature of risk based on the accident occurred on path.
In an alternative embodiment, the assessment system of vehicle risk calculates separately out the feature of risk based on speed
It characteristic value, the characteristic value of feature of risk based on acceleration, the characteristic value of feature of risk based on turning and is driven based on fatigue
Then the characteristic value for the feature of risk sailed is handled the characteristic value of above-mentioned each feature of risk, obtain based on behavior of driving
The risk of risk.Meanwhile the assessment system of vehicle risk calculate separately out the feature of risk based on drive time characteristic value,
The characteristic value of feature of risk, the characteristic value of feature of risk based on path length based on vehicle carrying and based on occurring on path
Accident feature of risk characteristic value, equally the characteristic value of above-mentioned each feature of risk is handled, is obtained based on driving
The risk of path risk.
It should be noted that the feature of risk based on speed can include but is not limited to the average speed of vehicle, vehicle
Travel speed and speed are more than the accounting of 80km/h.Wherein, speed is more than that the accounting of 80km/h is bigger, then based on the wind of speed
The risk of dangerous feature is bigger.
Average acceleration, anxious acceleration times when feature of risk based on acceleration can include but is not limited to vehicle driving
And number of bringing to a halt, wherein if acceleration is greater than 0.3m/s2, but it is less than 1.5m/s2, it is determined that vehicle is accelerating to go
It sails；If acceleration is greater than 1.5m/s2, it is determined that vehicle is carrying out anxious acceleration；If the acceleration of vehicle deceleration traveling is greater than
0.3m/s2, but it is less than 2m/s2, it is determined that vehicle brake；If the acceleration of vehicle deceleration traveling is greater than 2m/s2, it is determined that vehicle
It is bringing to a halt.The number for suddenly accelerating and bringing to a halt in vehicle travel process is determined according to the acceleration of vehicle.Its
In, anxious acceleration times or number of bringing to a halt are more, then the characteristic value of the feature of risk based on acceleration is bigger.
In addition, the feature of risk based on turning can include but is not limited to the turning speed of Ackermann steer angle, zig zag
Number, number of turns.Specifically, can determine whether vehicle takes a sudden turn according to the velocity magnitude of Ackermann steer angle.Wherein, anxious
Number of turns or number of turns are more, then the characteristic value of the feature of risk based on turning is bigger.
Feature of risk based on fatigue driving can include but is not limited to continuously drive duration and continuous driving range,
In, it can determine whether driver belongs to fatigue driving according to the change of vehicle location, for example, detecting in 4 hours, vehicle
Position change always, i.e., driver is driving always vehicle in four hours, at this point, confirmation driver belong to fatigue
It drives.In addition, whether can be changed according to the position of vehicle within a preset time (for example, in 5 minutes) to determine that vehicle is
It is no to be in driving status, if the position of vehicle within a preset time confirms that vehicle remains static there is no variation,
Driver is resting.Wherein, continuously drive that duration is longer or continuous driving range is longer, then based on the risk of fatigue driving
The characteristic value of feature is bigger.
In addition it is also necessary to which explanation, the route characteristic for path risk of driving can be adopted from the GPS positioning module of vehicle
It is obtained in the data and electronic road form data collected.The wind based on drive time can be obtained according to the GPS positioning module of vehicle
Dangerous feature, wherein the feature of risk based on drive time can include but is not limited to night running, traveling on daytime.And pass through electricity
Sub- road forms data can obtain the feature of risk carried based on vehicle, the feature of risk based on path length and based on occurring on path
Accident feature of risk.In addition, the feature of risk based on vehicle carrying includes but is not limited to the cargo dead-weight and overload feelings of vehicle
Condition.
In addition, passing through the characteristic value of available every kind risk of driving of step S204, wherein characteristic value be risk because
Son.As shown in the above, there is the support of mass data therefore to pass through step S204 for the acquisition of risks and assumptions or characteristic value
It can achieve the characteristic value with data supporting, and then accurate evaluation carried out to vehicle risk based on obtained characteristic value.
Step S206 determines the risk assessment value of vehicle based on the risk of at least one risk of driving.
Specifically, can be obtained driving the risk of behaviorist risk according to behavioural characteristic of driving, according to path risk of driving
Route characteristic can obtain driving the risk of path risk, then the risk to behaviorist risk of driving and path wind of driving again
The risk of danger, which carries out processing, can be obtained the risk assessment value of vehicle.After obtaining the risk assessment value of vehicle, vehicle risk
Assessment system the risk assessment value of vehicle is stored to online database, server is by api interface from online database
The risk assessment value of vehicle is got, staff or driver can be according to the risk assessment values for the vehicle that server is shown
Improve driving habits, and then improves the safe driving consciousness of driver.
Based on scheme defined by above-mentioned steps S202 to step S206, it can know, by obtain vehicle at least one
Class vehicle data, and the risk of existing at least one risk of driving when driving vehicle is obtained according at least a kind of vehicle data
Degree, the risk finally based at least one risk of driving determine the risk assessment value of vehicle.
Be easily noted that, due to acquisition be vehicle at least a kind of vehicle data, and vehicle data includes vehicle
The electronic road form data of data and vehicle are got in positioning on Crane Rail ready, i.e. the application is according to a variety of vehicle datas pair
Vehicle risk carries out comprehensive assessment, so as to accurately assess vehicle risk.Further, since the risk for risk of driving
Degree is obtained according to vehicle data, that is, the risk for risk of driving has a large amount of data supporting, and is not according to expert
Subjective experience, thereby further ensured that the accurate evaluation to vehicle risk.
As shown in the above, the application can achieve the purpose accurately assessed vehicle risk, to realize
Guarantee driver's safe driving, reduces the technical effect that traffic accident occurs, and then solve the prior art to vehicle risk
Assessment inaccuracy technical problem.
In an alternative embodiment, according to the risk of the available behaviorist risk of driving of behavioural characteristic of driving, root
According to the risk of the available path risk of driving of the route characteristic for path risk of driving, and before this, it is also necessary to each
Behavioural characteristic of driving and/or each route characteristic are pre-processed, wherein pretreatment includes at least one following: at normalization
Reason and filtration treatment.
It should be noted that above-mentioned filtration treatment is based on machine learning algorithm to each behavioural characteristic and/or every of driving
A route characteristic is filtered processing, obtains key risk feature.Wherein, machine learning algorithm can be but be not limited to decision
Tree, random forest, XGBoosting scheduling algorithm.For example, value-at-risk can be filtered out greater than preset threshold by decision Tree algorithms
Feature of risk as key risk feature.
In addition, drive each of after normalization can be calculated by following formula behavioural characteristic and/or each path spy
The characteristic value of sign:
In above formula,For the characteristic value of drive behavioural characteristic or each route characteristic each of after normalization, x is risk
The characteristic value of feature, avg (x) are characterized the average value of value, and s (x) is characterized the standard deviation of value.
In addition it is also necessary to explanation, to driving behavioural characteristic and/or when route characteristic pre-processes, can first carry out
Normalized, then it is filtered processing, filtration treatment can also be first carried out, then be normalized.I.e. normalized with
The sequence of filtration treatment is unlimited.
It in an alternative embodiment, can be according to driving after being pre-processed to each behavioural characteristic of driving
The risk of the available behaviorist risk of driving of behavioural characteristic, wherein as shown in figure 3, being driven according to behavioural characteristic of driving
The method of the risk of behaviorist risk includes the following steps:
Step S302 gets data ready using positioning of the vehicle on Crane Rail, calculates the spy for behavioural characteristic of each driving
Value indicative；
Step S304, the characteristic value based at least one behavioural characteristic of driving carry out the training of the first logic, generate vehicle
It drives the risk of behaviorist risk.
It should be noted that above-mentioned first logic is trained for the model being trained to the characteristic value for behavioural characteristic of driving.
Specifically, the characteristic value of the feature of risk based on speed is determined according to speed is more than the accounting of 80 mileages, according to
Anxious acceleration times and/or number of bringing to a halt determine the characteristic value of the feature of risk based on acceleration, according to number of turns and/or
Turning speed determines the characteristic value of the feature of risk based on turning, drives duration and/or continuous driving range is true according to continuous
The characteristic value of the fixed feature of risk based on fatigue driving.After the characteristic value for obtaining above-mentioned each feature of risk, to characteristic value
It is trained, the risk of the behaviorist risk of driving of vehicle can be obtained.
In an alternative embodiment, as shown in figure 4, according at least a kind of vehicle data, when obtaining driving vehicle
The method of the risk of existing at least one risk of driving includes the following steps:
Step S402 gets data and electronic road form data ready using positioning of the vehicle on Crane Rail, is calculated every
The characteristic value of a route characteristic；
Step S402 carries out the training of the second logic based at least one route characteristic, generates the path risk of driving of vehicle
Risk.
It should be noted that above-mentioned second logic is trained for the mould being trained to the route characteristic value for path risk of driving
Type.
Specifically, the characteristic value of the feature of risk based on drive time is determined according to the period driven, for example,
Characteristic value between 6 points to 6 points of evening of morning is less than at late 6 points to 6 points of characteristic value of morning next day；According to the cargo dead-weight of vehicle
How many characteristic values to determine the feature of risk based on vehicle carrying, wherein more in cargo dead-weight, characteristic value is bigger；According to row
Mileage is sailed to determine the characteristic value of the feature of risk based on path length；It is determined according to the frequency that accident occurs based on path
The characteristic value of the feature of risk of the accident of generation.After the characteristic value for obtaining above-mentioned each feature of risk, characteristic value is carried out
Training, can be obtained the risk of the path risk of driving of vehicle.
It should be noted that after the risk for obtaining at least one risk of driving, driven risk according at least one
Risk can determine the risk assessment value of vehicle, wherein can the risk to risk of driving carry out averaging calculating, obtain
The risk assessment value of vehicle.Formula specific as follows:
In above formula,For the risk assessment value of vehicle, S1For the risk for behaviorist risk of driving, S2For path wind of driving
The risk of danger.
In an alternative embodiment, in the case where the vehicle data of vehicle further includes the insurance data of vehicle,
According at least a kind of vehicle data, obtain when driving vehicle before the risk of existing at least one risk of driving, vehicle
The appraisal procedure of risk further include: obtain at least one vehicle that insurance number is more than pre-determined number, and be more than to insurance number
The vehicle of pre-determined number carries out risk assessment.
Specifically, the assessment system of vehicle risk will have 2 times and the vehicle of above insurance is as positive sample, and protect
Vehicle of the dangerous number less than 2 times carries out risk assessment as negative sample, and to the vehicle of positive sample.Wherein, vehicle risk is assessed
Method specifically include to obtain respectively and drive the risk of behaviorist risk and the risk for path risk of driving.
In an alternative embodiment, logistics is carried out to the characteristic value of at least one behavioural characteristic of driving
Regression training, the Claims Resolution probability of each vehicle is obtained by logistics regression, and will be every after training
The Claims Resolution probability of vehicle carries out Regularization, by its Regularization to 0-100, and then the wind for the behaviorist risk that obtains driving
Dangerous degree.
In an alternative embodiment, the characteristic value of the route characteristic of at least one path risk of driving is carried out
Logistics regression training, the Claims Resolution for obtaining each vehicle after training by logistics regression are general
Rate, and the Claims Resolution probability of each vehicle is subjected to Regularization, by its Regularization to 0-100, and then obtain road of driving
The risk of diameter risk.
It should be noted that after the risk for the behaviorist risk that obtains driving and the risk for path risk of driving, together
The risk to behaviorist risk of driving can be used in sample and the risk for path risk of driving carries out the method for being averaging calculating, obtains
The risk assessment value of vehicle.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the application is not limited by the described action sequence because
According to the application, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, related actions and modules not necessarily the application
It is necessary.
Through the above description of the embodiments, those skilled in the art can be understood that according to above-mentioned implementation
The appraisal procedure of the vehicle risk of example can be realized by means of software and necessary general hardware platform, naturally it is also possible to logical
Hardware is crossed, but the former is more preferably embodiment in many cases.Based on this understanding, the technical solution of the application is substantially
The part that contributes to existing technology can be embodied in the form of software products in other words, which deposits
Storage in a storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (can be with
It is mobile phone, computer, server or the network equipment etc.) execute method described in each embodiment of the application.
Embodiment 2
According to the embodiment of the present application, a kind of interface equipment is additionally provided, it should commenting for the vehicle risk in above-described embodiment 1
Estimate method and api interface service is provided, wherein api interface service can be but be not limited to API service shown in Fig. 1 (a).
Embodiment 3
According to the embodiment of the present application, additionally provide a kind of for implementing the vehicle risk of the appraisal procedure of above-mentioned vehicle risk
Assessment device, as shown in figure 5, the device includes: that the first acquisition module 501, second obtains module 503 and determining module
505。
Wherein, first module 501 is obtained, for obtaining at least a kind of vehicle data of vehicle；Second obtains module 503,
For obtaining the risk of existing at least one risk of driving when driving vehicle according at least a kind of vehicle data；Determine mould
Block 505 determines the risk assessment value of vehicle for the risk based at least one risk of driving.
Herein it should be noted that above-mentioned first acquisition module 501, second obtains module 503 and determining module 505 is right
Should be in the step S202 to step S206 in embodiment 1, example and application scenarios that three modules and corresponding step are realized
It is identical, but it is not limited to the above embodiments a disclosure of that.
It should be noted that the vehicle data of vehicle includes at least one following: positioning of the vehicle on Crane Rail is beaten
The electronic road form data of point data and vehicle.
It should be noted that risk of driving includes at least: drive behaviorist risk and path risk of driving, wherein be used for table
Sign drive behaviorist risk behavioural characteristic of driving include at least it is one of following: feature of risk based on speed, based on acceleration
Feature of risk, the feature of risk based on turning and the feature of risk based on fatigue driving；For characterizing the road for path risk of driving
Diameter feature includes at least: feature of risk based on drive time, the feature of risk based on vehicle carrying, based on the wind of path length
Dangerous feature and feature of risk based on the accident occurred on path.
In an alternative embodiment, the second acquisition module includes: the first computing module and the first generation module.Its
In, the first computing module calculates behavioural characteristic of each driving for getting data ready using positioning of the vehicle on Crane Rail
Characteristic value；First generation module carries out the training of the first logic for the characteristic value based at least one behavioural characteristic of driving, generates
The risk of the behaviorist risk of driving of vehicle.
Herein it should be noted that above-mentioned first computing module and the first generation module correspond to the step in embodiment 1
Rapid S302 to step S304, two modules are identical as example and application scenarios that corresponding step is realized, but are not limited to above-mentioned
One disclosure of that of embodiment.
In an alternative embodiment, the second acquisition module includes: the second computing module and the second generation module.Its
In, the second computing module is calculated for getting data and electronic road form data ready using positioning of the vehicle on Crane Rail
The characteristic value of each route characteristic；Second generation module, it is raw for carrying out the training of the second logic based at least one route characteristic
At the risk of the path risk of driving of vehicle.
Herein it should be noted that above-mentioned second computing module and the second generation module correspond to the step in embodiment 1
Rapid S402 to step S404, two modules are identical as example and application scenarios that corresponding step is realized, but are not limited to above-mentioned
One disclosure of that of embodiment.
In an alternative embodiment, the assessment device of vehicle risk further include: preprocessing module.Wherein, it pre-processes
Module, for driving behavioural characteristic and/or each route characteristic pre-processes to each, wherein pretreatment include such as down toward
It is one of few: normalized and filtration treatment.
It should be noted that filtration treatment is based on machine learning algorithm to each behavioural characteristic and/or each road of driving
Diameter feature is filtered processing, obtains key risk feature.
In an alternative embodiment, the assessment device of vehicle risk further include: the first evaluation module.Wherein, first
In the case that evaluation module for the vehicle data in vehicle further includes the insurance data of vehicle, it is more than pre- for obtaining insurance number
Determine at least one vehicle of number, and risk assessment is carried out to the vehicle that insurance number is more than pre-determined number.
In an alternative embodiment, determining module includes: the second evaluation module.Wherein, the second evaluation module is used for
Averaging calculating is carried out to the risk for risk of driving, obtains the risk assessment value of vehicle.
Embodiment 4
According to the embodiment of the present application, additionally provide it is a kind of for implementing the system of the appraisal procedure of above-mentioned vehicle risk,
In, which includes processor and memory.
Memory is connect with processor, for providing the instruction for handling following processing step for processor:
Obtain at least a kind of vehicle data of vehicle；
According at least a kind of vehicle data, the risk of existing at least one risk of driving when driving vehicle is obtained；
Based on the risk of at least one risk of driving, the risk assessment value of vehicle is determined.
From the foregoing, it will be observed that being driven by at least a kind of vehicle data for obtaining vehicle, and according at least a kind of vehicle data acquisition
The risk of existing at least one risk of driving when sailing vehicle, the risk finally based at least one risk of driving determine
The risk assessment value of vehicle.
Be easily noted that, due to acquisition be vehicle at least a kind of vehicle data, and vehicle data includes vehicle
The electronic road form data of data and vehicle are got in positioning on Crane Rail ready, i.e. the application is according to a variety of vehicle datas pair
Vehicle risk carries out comprehensive assessment, so as to accurately assess vehicle risk.Further, since the risk for risk of driving
Degree is obtained according to vehicle data, that is, the risk for risk of driving has a large amount of data supporting, and is not according to expert
Subjective experience, thereby further ensured that the accurate evaluation to vehicle risk.
As shown in the above, the application can achieve the purpose accurately assessed vehicle risk, to realize
Guarantee driver's safe driving, reduces the technical effect that traffic accident occurs, and then solve the prior art to vehicle risk
Assessment inaccuracy technical problem.
Embodiment 5
According to the embodiment of the present application, a kind of storage medium is additionally provided, which includes the program of storage, wherein
In program operation, equipment where control storage medium executes the appraisal procedure of the vehicle risk in embodiment 1.
Embodiment 6
According to the one aspect of the embodiment of the present application, a kind of processor is additionally provided, which is used to run program,
In, the appraisal procedure of the vehicle risk in embodiment 1 is executed when program is run.
Embodiment 7
According to the one aspect of the embodiment of the present application, a kind of terminal is additionally provided, comprising: data acquisition unit, for obtaining
The data of at least a kind of vehicle of pick-up；Processor, processor run program, wherein for adopting from data when program is run
The data for collecting unit output execute the appraisal procedure of the vehicle risk in embodiment 1.
Embodiment 8
According to the one aspect of the embodiment of the present application, a kind of terminal is additionally provided, comprising: data acquisition unit, for obtaining
The data of at least a kind of vehicle of pick-up；Storage medium, for storing program, wherein program is at runtime for from data
The data of acquisition unit output execute the appraisal procedure of the vehicle risk in embodiment 1.
Embodiment 9
Embodiments herein can provide a kind of terminal, which can be in terminal group
Any one computer terminal.Optionally, in the present embodiment, above-mentioned terminal also could alternatively be mobile whole
The terminal devices such as end.
Optionally, in the present embodiment, above-mentioned terminal can be located in multiple network equipments of computer network
At least one network equipment.
Fig. 6 shows a kind of hardware block diagram of terminal.As shown in fig. 6, terminal A may include one
(processor 162 may include but unlimited for a or multiple (162a, 162b ... ... being used in figure, 162n is shown) processor 162
In the processing unit of Micro-processor MCV or programmable logic device FPGA etc.), memory 164, Yi Jiyong for storing data
In the transmitting device 166 of communication function.In addition to this, it can also include: display, input/output interface (I/O interface), lead to
With the port universal serial bus (USB) (can be used as a port in the port of I/O interface is included), network interface, power supply and/
Or camera.It will appreciated by the skilled person that structure shown in fig. 6 is only to illustrate, not to above-mentioned electronic device
Structure cause to limit.For example, terminal A may also include the more perhaps less component than shown in Fig. 6 or have
The configuration different from shown in Fig. 6.
It is to be noted that said one or multiple processors 162 and/or other data processing circuits lead to herein
Can often " data processing circuit " be referred to as.The data processing circuit all or part of can be presented as software, hardware, firmware
Or any other combination.In addition, data processing circuit for single independent processing module or all or part of can be integrated to meter
In any one in other elements in calculation machine terminal A.As involved in the embodiment of the present application, the data processing circuit
(such as the selection for the variable resistance end path connecting with interface) is controlled as a kind of processor.
Processor 162 can call the information and application program of memory storage by transmitting device, to execute following steps
It is rapid: to obtain at least a kind of vehicle data of vehicle；According at least a kind of vehicle data, obtain existing at least one when driving vehicle
Plant the risk for risk of driving；Based on the risk of at least one risk of driving, the risk assessment value of vehicle is determined.
Memory 164 can be used for storing the software program and module of application software, such as the vehicle in the embodiment of the present application
Corresponding program instruction/the data storage device of the appraisal procedure of risk, processor 162 are stored in memory 164 by operation
Software program and module realize commenting for above-mentioned vehicle risk thereby executing various function application and data processing
Estimate method.Memory 164 may include high speed random access memory, may also include nonvolatile memory, such as one or more magnetic
Property storage device, flash memory or other non-volatile solid state memories.In some instances, memory 164 can further comprise
The memory remotely located relative to processor 162, these remote memories can pass through network connection to terminal A.
The example of above-mentioned network includes but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Transmitting device 166 is used to that data to be received or sent via a network.Above-mentioned network specific example may include
The wireless network that the communication providers of terminal A provide.In an example, transmitting device 166 includes that a network is suitable
Orchestration (Network Interface Controller, NIC), can be connected by base station with other network equipments so as to
Internet is communicated.In an example, transmitting device 166 can be radio frequency (Radio Frequency, RF) module,
For wirelessly being communicated with internet.
Display can such as touch-screen type liquid crystal display (LCD), the liquid crystal display aloow user with
The user interface of terminal A interacts.
Herein it should be noted that in some optional embodiments, above-mentioned terminal A shown in fig. 6 may include
Hardware element (including circuit), software element (including the computer code that may be stored on the computer-readable medium) or hardware member
The combination of both part and software element.It should be pointed out that Fig. 6 is only an example of particular embodiment, and it is intended to show
It may be present in the type of the component in above-mentioned terminal A out.
Processor can call the information and application program of memory storage by transmitting device, to execute following step:
Obtain at least a kind of vehicle data of vehicle；According at least a kind of vehicle data, existing at least one when driving vehicle is obtained
The risk for risk of driving；Based on the risk of at least one risk of driving, the risk assessment value of vehicle is determined.
Processor can call the information and application program of memory storage by transmitting device, to execute following step:
Data are got ready using positioning of the vehicle on Crane Rail, calculate the characteristic value for behavioural characteristic of each driving；Based at least one
The characteristic value for behavioural characteristic of driving carries out the training of the first logic, generates the risk of the behaviorist risk of driving of vehicle.
Processor can call the information and application program of memory storage by transmitting device, to execute following step:
Data and electronic road form data are got ready using positioning of the vehicle on Crane Rail, and the feature of each route characteristic is calculated
Value；The training of the second logic is carried out based at least one route characteristic, generates the risk of the path risk of driving of vehicle.
Processor can call the information and application program of memory storage by transmitting device, to execute following step:
Behavioural characteristic is driven and/or each route characteristic pre-processes to each, wherein pretreatment includes at least one following: being returned
One changes processing and filtration treatment.
Processor can call the information and application program of memory storage by transmitting device, to execute following step:
At least one vehicle that insurance number is more than pre-determined number is obtained, and risk is carried out to the vehicle that insurance number is more than pre-determined number
Assessment.
Processor can call the information and application program of memory storage by transmitting device, to execute following step:
Averaging calculating is carried out to the risk for risk of driving, obtains the risk assessment value of vehicle.
It will appreciated by the skilled person that structure shown in fig. 6 is only to illustrate, terminal is also possible to intelligence
It can mobile phone (such as Android phone, iOS mobile phone), tablet computer, applause computer and mobile internet device (Mobile
Internet Devices, MID), the terminal devices such as PAD.Fig. 6 it does not cause to limit to the structure of above-mentioned electronic device.Example
Such as, terminal A may also include the more or less component (such as network interface, display device) than shown in Fig. 6, or
Person has the configuration different from shown in Fig. 6.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing the relevant hardware of terminal device by program, which can store in a computer readable storage medium
In, storage medium may include: flash disk, read-only memory (Read-Only Memory, ROM), random access device (Random
Access Memory, RAM), disk or CD etc..
Embodiment 10
Embodiments herein additionally provides a kind of storage medium.Optionally, in the present embodiment, above-mentioned storage medium can
With program code performed by the appraisal procedure for saving vehicle risk provided by above-described embodiment one.
Optionally, in the present embodiment, above-mentioned storage medium can be located in computer network in computer terminal group
In any one terminal, or in any one mobile terminal in mobile terminal group.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: obtaining
At least a kind of vehicle data of pick-up；According at least a kind of vehicle data, obtains existing at least one when driving vehicle and drive
The risk of vehicle risk；Based on the risk of at least one risk of driving, the risk assessment value of vehicle is determined.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: making
Data are got ready with positioning of the vehicle on Crane Rail, calculate the characteristic value for behavioural characteristic of each driving；It is driven based at least one
The characteristic value that garage is characterized carries out the training of the first logic, generates the risk of the behaviorist risk of driving of vehicle.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: making
Data and electronic road form data are got ready with positioning of the vehicle on Crane Rail, and the characteristic value of each route characteristic is calculated；
The training of the second logic is carried out based at least one route characteristic, generates the risk of the path risk of driving of vehicle.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: right
Behavioural characteristic of each driving and/or each route characteristic are pre-processed, wherein pretreatment includes at least one following: normalizing
Change processing and filtration treatment.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: obtaining
Dangerous number of going bail for is more than at least one vehicle of pre-determined number, and carries out risk to the vehicle that insurance number is more than pre-determined number and comment
Estimate.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: right
The risk for risk of driving carries out averaging calculating, obtains the risk assessment value of vehicle.
Above-mentioned the embodiment of the present application serial number is for illustration only, does not represent the advantages or disadvantages of the embodiments.
In above-described embodiment of the application, all emphasizes particularly on different fields to the description of each embodiment, do not have in some embodiment
The part of detailed description, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others
Mode is realized.Wherein, the apparatus embodiments described above are merely exemplary, such as the division of the unit, only
A kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or components can combine or
Person is desirably integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Between coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or communication link of unit or module
It connects, can be electrical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can for personal computer, server or network equipment etc.) execute each embodiment the method for the application whole or
Part steps.And storage medium above-mentioned includes: that USB flash disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), mobile hard disk, magnetic or disk etc. be various to can store program code
Medium.
The above is only the preferred embodiment of the application, it is noted that for the ordinary skill people of the art
For member, under the premise of not departing from the application principle, several improvements and modifications can also be made, these improvements and modifications are also answered
It is considered as the protection scope of the application.
Claims (16)
1. a kind of appraisal procedure of vehicle risk characterized by comprising
Obtain at least a kind of vehicle data of vehicle；
According at least a kind of vehicle data, the risk of existing at least one risk of driving when driving the vehicle is obtained
Degree；
Based on the risk of at least one risk of driving, the risk assessment value of the vehicle is determined.
2. the method according to claim 1, wherein the vehicle data of the vehicle includes at least one following:
The electronic road form data of data and the vehicle are got in positioning of the vehicle on Crane Rail ready.
3. according to the method described in claim 2, it is characterized in that, the risk of driving includes at least: drive behaviorist risk and
It drives path risk, wherein the behavioural characteristic of driving for characterizing the behaviorist risk of driving includes at least one of following: being based on
The feature of risk of speed, the feature of risk based on acceleration, the feature of risk based on turning and the risk based on fatigue driving are special
Sign；Route characteristic for characterizing the path risk of driving includes at least: feature of risk based on drive time, based on vehicle
The feature of risk of carrying, the feature of risk based on path length and the feature of risk based on the accident occurred on path.
4. according to the method described in claim 3, it is characterized in that, obtaining according at least a kind of vehicle data and driving institute
The risk of existing at least one risk of driving when stating vehicle, comprising:
Data are got ready using positioning of the vehicle on Crane Rail, calculate the characteristic value for behavioural characteristic of each driving；
Characteristic value based at least one behavioural characteristic of driving carries out the training of the first logic, generates the behavior wind of driving of the vehicle
The risk of danger.
5. according to the method described in claim 3, it is characterized in that, obtaining according at least a kind of vehicle data and driving institute
The risk of existing at least one risk of driving when stating vehicle, comprising:
Data and the electronic road form data are got ready using positioning of the vehicle on Crane Rail, and each path is calculated
The characteristic value of feature；
The training of the second logic is carried out based at least one route characteristic, generates the risk of the path risk of driving of the vehicle.
6. the method according to any one of claim 3 to 5, which is characterized in that each behavioural characteristic of driving
And/or each route characteristic is pre-processed, wherein the pretreatment includes at least one following: normalized and
Filtration treatment.
7. according to the method described in claim 6, it is characterized in that, the filtration treatment is based on machine learning algorithm to each
Drive behavioural characteristic and/or each route characteristic progress filtration treatment, obtain key risk feature.
8. according to the method described in claim 2, it is characterized in that, the vehicle vehicle data further include vehicle insurance
In the case where data, according at least a kind of vehicle data, obtains existing at least one when driving the vehicle and drive
Before the risk of risk, the method also includes: at least one vehicle that insurance number is more than pre-determined number is obtained, and to institute
It states the vehicle that insurance number is more than pre-determined number and carries out risk assessment.
9. the method according to claim 1, wherein the risk based at least one risk of driving, really
The risk assessment value of the fixed vehicle, comprising: averaging calculating is carried out to the risk of the risk of driving, obtains the vehicle
Risk assessment value.
10. a kind of interface equipment, which is characterized in that provide api interface for method described in any one of claim 1 to 9
Service.
11. a kind of assessment device of vehicle risk characterized by comprising
First obtains module, for obtaining at least a kind of vehicle data of vehicle；
Second obtains module, for obtaining existing at least one when driving the vehicle according at least a kind of vehicle data
Plant the risk for risk of driving；
Determining module determines the risk assessment value of the vehicle for the risk based at least one risk of driving.
12. a kind of storage medium, which is characterized in that the storage medium includes the program of storage, wherein run in described program
When control the storage medium where equipment perform claim require any one of 1 to 9 described in vehicle risk appraisal procedure.
13. a kind of processor, which is characterized in that the processor is for running program, wherein right of execution when described program is run
Benefit require any one of 1 to 9 described in vehicle risk appraisal procedure.
14. a kind of terminal characterized by comprising
Data acquisition unit, the data of at least a kind of vehicle for obtaining vehicle；
Processor, the processor run program, wherein for exporting from the data acquisition unit when described program is run
The appraisal procedure of vehicle risk described in any one of data perform claim requirement 1 to 9.
15. a kind of terminal characterized by comprising
Data acquisition unit, the data of at least a kind of vehicle for obtaining vehicle；
Storage medium, for storing program, wherein the described program number for being exported from the data acquisition unit at runtime
According to the appraisal procedure of vehicle risk described in any one of perform claim requirement 1 to 9.
16. a kind of system characterized by comprising
Processor；And
Memory is connected to the processor, for providing the instruction for handling following processing step for the processor:
Obtain at least a kind of vehicle data of vehicle；
According at least a kind of vehicle data, the risk of existing at least one risk of driving when driving the vehicle is obtained
Degree；
Based on the risk of at least one risk of driving, the risk assessment value of the vehicle is determined.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711059783.0A CN109754595B (en) | 2017-11-01 | 2017-11-01 | Vehicle risk assessment method and device and interface equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711059783.0A CN109754595B (en) | 2017-11-01 | 2017-11-01 | Vehicle risk assessment method and device and interface equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109754595A true CN109754595A (en) | 2019-05-14 |
| CN109754595B CN109754595B (en) | 2022-02-01 |
Family
ID=66399285
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711059783.0A Active CN109754595B (en) | 2017-11-01 | 2017-11-01 | Vehicle risk assessment method and device and interface equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109754595B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110682875A (en) * | 2019-09-19 | 2020-01-14 | 中国第一汽车股份有限公司 | Vehicle safety risk assessment method and device and vehicle |
| CN111007726A (en) * | 2019-12-24 | 2020-04-14 | 斑马网络技术有限公司 | Starter ablation risk assessment method and device and electronic equipment |
| CN112258837A (en) * | 2020-10-19 | 2021-01-22 | 腾讯科技（深圳）有限公司 | Vehicle early warning method, related device, equipment and storage medium |
| CN112270114A (en) * | 2020-11-24 | 2021-01-26 | 武汉理工大学 | Vehicle personalized risk behavior identification method |
| WO2021063005A1 (en) * | 2019-09-30 | 2021-04-08 | 上海商汤临港智能科技有限公司 | Driving data analysis method and apparatus, electronic device and computer storage medium |
| CN113469501A (en) * | 2021-06-07 | 2021-10-01 | 杭州搜车数据科技有限公司 | Data analysis method and device, electronic device and computer equipment |
| CN113657733A (en) * | 2021-08-02 | 2021-11-16 | 东风柳州汽车有限公司 | Method, device, equipment and storage medium for managing new automobile product problem points |
| CN114707915A (en) * | 2022-06-06 | 2022-07-05 | 苏交科集团股份有限公司 | Vehicle transportation risk assessment system and method |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2327210A1 (en) * | 2000-12-01 | 2002-06-01 | Accu-Star Systems, Inc. | System and method for facilitating shipment transaction, creation and monitoring |
| CN105374211A (en) * | 2015-12-09 | 2016-03-02 | 敏驰信息科技（上海）有限公司 | System and method for calculating driving risks and assisting automobile insurance pricing based on multi-source data |
| CN105512453A (en) * | 2014-10-15 | 2016-04-20 | 厦门雅迅网络股份有限公司 | Method and device for vehicle risk judgment based on historical mileages |
| CN105574537A (en) * | 2015-11-23 | 2016-05-11 | 北京高科中天技术股份有限公司 | Multi-sensor-based dangerous driving behavior detection and evaluation method |
| CN105574699A (en) * | 2015-12-31 | 2016-05-11 | 郑州烽火台电子有限公司 | Hazardous material transportation vehicle comprehensive management system |
| CN106095963A (en) * | 2016-06-17 | 2016-11-09 | 上海经达信息科技股份有限公司 | Vehicle drive behavior analysis big data public service platform under the Internet+epoch |
| CN106127586A (en) * | 2016-06-17 | 2016-11-16 | 上海经达信息科技股份有限公司 | Vehicle insurance rate aid decision-making system under big data age |
| CN106156877A (en) * | 2015-04-21 | 2016-11-23 | 阿里巴巴集团控股有限公司 | Predict the drive method of risk, Apparatus and system |
| CN106651162A (en) * | 2016-12-09 | 2017-05-10 | 思建科技有限公司 | Big data-based driving risk assessment method |
| CN106683395A (en) * | 2016-12-29 | 2017-05-17 | 广州交通信息化建设投资营运有限公司 | Dedicated vehicle scheduling information system for large-scale sports meet |
| CN106855965A (en) * | 2016-08-16 | 2017-06-16 | 芮锶钶（上海）网络技术有限公司 | A kind of driving run-length data based on motor vehicle assesses the method that it drives risk |
| CN106952002A (en) * | 2017-04-05 | 2017-07-14 | 南京人人保网络技术有限公司 | Driving methods of risk assessment and device based on driving behavior |
-
2017
- 2017-11-01 CN CN201711059783.0A patent/CN109754595B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2327210A1 (en) * | 2000-12-01 | 2002-06-01 | Accu-Star Systems, Inc. | System and method for facilitating shipment transaction, creation and monitoring |
| CN105512453A (en) * | 2014-10-15 | 2016-04-20 | 厦门雅迅网络股份有限公司 | Method and device for vehicle risk judgment based on historical mileages |
| CN106156877A (en) * | 2015-04-21 | 2016-11-23 | 阿里巴巴集团控股有限公司 | Predict the drive method of risk, Apparatus and system |
| CN105574537A (en) * | 2015-11-23 | 2016-05-11 | 北京高科中天技术股份有限公司 | Multi-sensor-based dangerous driving behavior detection and evaluation method |
| CN105374211A (en) * | 2015-12-09 | 2016-03-02 | 敏驰信息科技（上海）有限公司 | System and method for calculating driving risks and assisting automobile insurance pricing based on multi-source data |
| CN105574699A (en) * | 2015-12-31 | 2016-05-11 | 郑州烽火台电子有限公司 | Hazardous material transportation vehicle comprehensive management system |
| CN106095963A (en) * | 2016-06-17 | 2016-11-09 | 上海经达信息科技股份有限公司 | Vehicle drive behavior analysis big data public service platform under the Internet+epoch |
| CN106127586A (en) * | 2016-06-17 | 2016-11-16 | 上海经达信息科技股份有限公司 | Vehicle insurance rate aid decision-making system under big data age |
| CN106855965A (en) * | 2016-08-16 | 2017-06-16 | 芮锶钶（上海）网络技术有限公司 | A kind of driving run-length data based on motor vehicle assesses the method that it drives risk |
| CN106651162A (en) * | 2016-12-09 | 2017-05-10 | 思建科技有限公司 | Big data-based driving risk assessment method |
| CN106683395A (en) * | 2016-12-29 | 2017-05-17 | 广州交通信息化建设投资营运有限公司 | Dedicated vehicle scheduling information system for large-scale sports meet |
| CN106952002A (en) * | 2017-04-05 | 2017-07-14 | 南京人人保网络技术有限公司 | Driving methods of risk assessment and device based on driving behavior |
Non-Patent Citations (1)
| Title |
|---|
| 刘子英 等: "完善车辆GPS系统应用功能提高交通管理智能水平", 《安全责任 重在落实——第四届吉林安全生产论坛论文集》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110682875A (en) * | 2019-09-19 | 2020-01-14 | 中国第一汽车股份有限公司 | Vehicle safety risk assessment method and device and vehicle |
| WO2021063005A1 (en) * | 2019-09-30 | 2021-04-08 | 上海商汤临港智能科技有限公司 | Driving data analysis method and apparatus, electronic device and computer storage medium |
| CN111007726A (en) * | 2019-12-24 | 2020-04-14 | 斑马网络技术有限公司 | Starter ablation risk assessment method and device and electronic equipment |
| CN112258837A (en) * | 2020-10-19 | 2021-01-22 | 腾讯科技（深圳）有限公司 | Vehicle early warning method, related device, equipment and storage medium |
| CN112258837B (en) * | 2020-10-19 | 2024-04-12 | 腾讯科技（深圳）有限公司 | Vehicle early warning method, related device, equipment and storage medium |
| CN112270114A (en) * | 2020-11-24 | 2021-01-26 | 武汉理工大学 | Vehicle personalized risk behavior identification method |
| CN112270114B (en) * | 2020-11-24 | 2022-04-29 | 武汉理工大学 | Vehicle personalized risk behavior identification method |
| CN113469501A (en) * | 2021-06-07 | 2021-10-01 | 杭州搜车数据科技有限公司 | Data analysis method and device, electronic device and computer equipment |
| CN113657733A (en) * | 2021-08-02 | 2021-11-16 | 东风柳州汽车有限公司 | Method, device, equipment and storage medium for managing new automobile product problem points |
| CN113657733B (en) * | 2021-08-02 | 2023-10-24 | 东风柳州汽车有限公司 | Method, device, equipment and storage medium for managing new product problem points of automobile |
| CN114707915A (en) * | 2022-06-06 | 2022-07-05 | 苏交科集团股份有限公司 | Vehicle transportation risk assessment system and method |
| CN114707915B (en) * | 2022-06-06 | 2022-08-12 | 苏交科集团股份有限公司 | Vehicle transportation risk assessment system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109754595B (en) | 2022-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109754595A (en) | Appraisal procedure, device and the interface equipment of vehicle risk | |
| CN109844793B (en) | OEM line assembly system based on intelligent and remote information processing and corresponding method thereof | |
| CN109690606B (en) | System based on remote information processing and corresponding method thereof | |
| US12014424B2 (en) | Autonomous vehicle premium computation using predictive models | |
| CN106170826B (en) | The monitoring method and system of cab-getter's number | |
| US9767622B2 (en) | System and a method for improved car prognosis | |
| CN105261229B (en) | A kind of monitoring method and system of business's concrete car | |
| CN109270565B (en) | Processing device for vehicle-mounted GPS big data | |
| JP2019530917A (en) | Intelligent self-adaptive vehicular device and its corresponding method for dynamic score-based risk measurement and aggregation using telematics connected search engine | |
| CN110751586A (en) | Order travel abnormity identification method and system | |
| US20140081675A1 (en) | Systems, methods, and apparatus for optimizing claim appraisals | |
| CN106548241A (en) | Net about car method for determining running state, apparatus and system | |
| EP2863282B1 (en) | System and method for detecting anomaly associated with driving a vehicle | |
| CN108944939B (en) | Method and system for providing driving directions | |
| CN110471411A (en) | Automatic Pilot method and servomechanism | |
| CN203444115U (en) | Remote positioning and monitoring system for logistics vehicles | |
| CN106573546A (en) | Presenting routing information for electric vehicles | |
| CN105659639A (en) | Associating external devices to vehicles and usage of said association | |
| CN112319489B (en) | Driving behavior monitoring method, driving behavior monitoring system, server and storage medium | |
| CN111259737B (en) | Method and device for predicting failure of steering wheel of vehicle, electronic equipment and storage medium | |
| CN113256993B (en) | Method for training and analyzing vehicle driving risk by model | |
| US20160078692A1 (en) | Method and system for sharing transport information | |
| CN109214258A (en) | Lose the detection method and device that the personnel that drive drive in violation of rules and regulations | |
| CN108088686A (en) | Test emulation method and apparatus, computer storage media and electronic equipment | |
| CN103546577A (en) | Method and system for achieving safe driving |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |