CN107862868A - A kind of method that track of vehicle prediction is carried out based on big data - Google Patents
A kind of method that track of vehicle prediction is carried out based on big data Download PDFInfo
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Abstract
The invention discloses a kind of method that track of vehicle prediction is carried out based on big data, including：The driving path of target vehicle is determined, history vehicle data is obtained, is extracted from history vehicle data：Basic data and correction data are travelled, by basic data and correction data, according to acquisition time from closely to far being ranked up, calculating target vehicle passes through first object site F1The first probability P1X, and from current site C to first object site F1Very first time T used1X.By the present invention, reduce the skill requirement to staff, and can while prediction locus predicted time, while structuring and unstructured data are uniformly processed, improve operating efficiency.
Description
Technical field
The present invention relates to track of vehicle to predict field, pre- based on big data progress track of vehicle more particularly, to one kind
The method of survey.
Background technology
Modern society, tracking often is escaped by the way of escape using driving after offender's crime, public security organ is chasing
When crime divides, on the one hand, to send car tracing offender, on the other hand, it is also desirable to which prejudging offender in advance can
The route of running away of energy, arranges police strength on most possible multiple routes, carries out interception in advance.
With the increase for relating to car crime, the angle that increasingly key is play in case cracking is investigated and seized for deploying to ensure effective monitoring and control of illegal activities for vehicle
Color.Currently pursue and capture suspected vehicles, it is more that its history driving trace is analyzed using the traditional approach for manually checking the data such as video, picture
Find the trip rule of target vehicle, or the driving inquired about by application system in the multiple special time period regional extents of certain car
Track, specific place of specific time is then summarised in by analysis deploys to ensure effective monitoring and control of illegal activities and intercept, workload is big, labor intensive material resources are more,
Efficiency is low, information delay, and higher for the skill requirement of staff, it is difficult to popularization and application, and to vehicle route
Prediction is time-consuming more, delays the time for chasing offender, even if in addition, many times predict the driving path of vehicle,
It is difficult to predict time of the vehicle by the driving path, cause to arrange police strength not in time, these defects are all chased to public security organ
Offender causes many difficulties.
On the other hand, the mode for capturing vehicle route data gradually increases, and includes video recording, picture and the biography of camera shooting
Manual record data of system etc., increasing for acquisition mode cause increasing sharply for data volume, majority of case, and people are to be difficult to
Short time quickly grasps so substantial amounts of data and is analyzed and summarized, so as to prejudge the track of vehicle.
Therefore it provides a kind of method that track of vehicle prediction can be carried out based on big data, reducing will to the experience of personnel
Ask, be this area urgent problem to be solved.
The content of the invention
In view of this, the invention provides a kind of method that track of vehicle prediction is carried out based on big data, solve existing
The problem of high to staff's skill requirement in technology.
In order to solve the above-mentioned technical problem, the present invention proposes a kind of method that track of vehicle prediction is carried out based on big data,
Including：
The driving path of target vehicle is determined, the driving path includes：The current site C and first of the target vehicle
History site O1, wherein, the first history site O1For a upper site adjacent with the current site C；
Any site is chosen as first object site F1；
History vehicle data is obtained, wherein, the history vehicle data includes the vehicle of multiple vehicles in preset time period
Data, the vehicle data include a vehicle driving path in the preset time period, the driving path, including：
Multiple sites and the acquisition time in the site；
Extracted from the history vehicle data：
Include the first history site O in the driving path successively1, the current site C and first is with reference to site A
The vehicle data, based on data, wherein：Described first with reference to site A is any site,
Include the first history site O in the driving path successively1, the current site C and the first object
Site F1The vehicle data, data as a comparison；
By the basic data, according to the acquisition time from closely to being far ranked up, before obtaining in the basic data
20% vehicle data, as first foundation group, obtain in the basic data close to after the first foundation group
30% vehicle data obtains last 50% vehicle data in the basic data, as the second base set
Three base sets；
By the correction data, according to the acquisition time from closely to being far ranked up, before obtaining in the correction data
20% vehicle data, as the first contrast groups, obtain in the correction data close to after first contrast groups
30% vehicle data obtains last 50% vehicle data in the correction data, as the second contrast groups
Three contrast groups；
The target vehicle is calculated using following equation and passes through the first object site F1The first probability P1X, and from work as
Preceding site C to the first object site F1Very first time T used1X：
P1X=(U11X*5+U12X*3+U13X*2)/(U11*5+U12*3+U13*2)
T1X=(T11X*5+T12X*3+T13X*2)/(T11*5+T12*3+T13*2)
Wherein：U11XIt is the number of vehicle data described in first contrast groups, U12XIt is institute in second contrast groups
State the number of vehicle data, U13XIt is the number of vehicle data described in the 3rd contrast groups, U11It is in the first foundation group
The number of the vehicle data, U12It is the number of vehicle data described in second base set, U13It is the 3rd base set
Described in vehicle data number, T11XBe in first contrast groups each vehicle from the current site C to the first object
Site F1The average used time, T12XBe in second contrast groups each vehicle from the current site C to the first object site
F1The average used time, T13XBe in the 3rd contrast groups each vehicle from the current site C to the first object site F1's
Average used time, T11It is average use of each vehicle from the current site C to described first with reference to site A in the first foundation group
When, T12Be in second base set each vehicle from the current site C to described first with reference to site A average used time, T13
It is average used time of each vehicle from the current site C to described first with reference to site A in the 3rd base set.
Optionally, in addition to：
Any site is chosen as the second target site F2；
Extracted from the history vehicle data：
Include the first history site O in the driving path successively1, the current site C, described first with reference to position
The references of the point A and second site B vehicle data, as master data, wherein：Described second with reference to site B is any bit
Point,
Include the first history site O in the driving path successively1, current site C, the first object position
Point F1With the second target site F2Vehicle data, as contrasting data；
By the master data, according to the acquisition time from closely to being far ranked up, before obtaining in the master data
20% vehicle data, as first basic group, obtain after being organized substantially close to described first in the master data
30% vehicle data, as second basic group, last 50% vehicle data in the master data is obtained, as
Three basic groups；
By the contrasting data, according to the acquisition time from closely to being far ranked up, before obtaining in the contrasting data
20% vehicle data, as the first control group, obtain in the contrasting data close to after first control group
30% vehicle data obtains last 50% vehicle data in the contrasting data, as the second control group
Three control groups；
The target vehicle is calculated using following equation and passes through the first object site F1With the second target site F2
The second probability P2X, and from the current site C pass through the first object site F1With the second target site F2Institute
Second time T2X：
P2X=(U21X*5+U22X*3+U23X*2)/(U21*5+U22*3+U23*2)
T2X=(T21X*5+T22X*3+T23X*2)/(T21*5+T22*3+T23*2)
Wherein：U21XIt is the number of vehicle data described in first control group, U22XIt is institute in second control group
State the number of vehicle data, U23XIt is the number of vehicle data described in the 3rd control group, U21It is in described first basic group
The number of the vehicle data, U22It is the number of vehicle data described in described second basic group, U23It is described 3rd basic group
Described in vehicle data number, T21XIt is that each vehicle passes through first mesh from the current site C in first control group
Mark point F1With the second target site F2The average used time, T12XBe in second control group each vehicle from the present bit
Point C passes through the first object site F1With the second target site F2The average used time, T23XIt is in the 3rd control group
Each vehicle passes through the first object site F from the current site C1With the second target site F2The average used time, T21
It is that each vehicle passes through the described first reference site A and the second reference position from the current site C in described first basic group
Point B average used time, T12It is that each vehicle passes through the described first reference site A from the current site C in described second basic group
With the described second reference site B average used time, T13It is that each vehicle passes through institute from the current site C in described 3rd basic group
Stated for the first average used time with reference to site A and described second with reference to site B.
Optionally, any site is being chosen as first object site F1Before, in addition to：From the institute of the target vehicle
State in driving path and obtain, with the first history site O1An adjacent upper site, as the second history site O2；
Now, in the driving path of the basic data and the correction data, all include successively：Described second goes through
History site O2, the first history site O1With the current site C.
Optionally, any site is being chosen as first object site F1Before, in addition to：From the institute of the target vehicle
State in driving path and obtain, with the first history site O1An adjacent upper site, as the second history site O2；
Now, the institute of the basic data, first master data, the correction data and first contrasting data
State in driving path, all include successively：The second history site O2, the first history site O1With the current site C.
Optionally, it is described acquisition history vehicle data, further for：
Obtain unstructured vehicle data；
The unstructured vehicle data are converted into structured vehicle data；
All structured vehicle data are obtained as the history vehicle data.
Optionally, the structured vehicle data storage is in the distributed data base system towards row, the database
System external provides unified interface；
It is described that the unstructured vehicle data are converted into structured vehicle data, further for：
The information of the unstructured vehicle data is extracted, described information is subjected to typing according to the form of the structure.
Optionally, the non-institutional vehicle data, including：Video, picture and voice；
The information of the extraction unstructured vehicle data, including：
Judge the classification of the unstructured vehicle data；
When the non-structural vehicle data is the video, the frame picture in the video is extracted, using picture recognition
Method obtains the information in the frame picture；
When the non-structural vehicle data is the picture, obtained using the image identification method in the frame picture
Information；
When non-structural vehicle data is voice, the voice is converted into by word using speech recognition system, described in acquisition
Information in word.
Optionally, the vehicle data, in addition to：The number-plate number.
Compared with prior art, a kind of method that track of vehicle prediction is carried out based on big data provided by the invention, is realized
Following beneficial effect：
A kind of method that track of vehicle prediction is carried out based on big data is provided, will to personnel's rawness using this method
Ask, solve the problems, such as it is high to staff's skill requirement in the prior art, and using this method can prediction vehicle row
While sailing path, prediction vehicle drives to the time in the path, solves the problems, such as to be difficult to predicted time in the prior art, this
Unified interface is used in method, unstructured data is uniformly converted into structural data, make each separate sources, form
Vehicle data conveniently accesses analysis system, reduces the complexity of data access, improves the accessible scope of data.
By referring to the drawings to the present invention exemplary embodiment detailed description, further feature of the invention and its
Advantage will be made apparent from.
Brief description of the drawings
It is combined in the description and the accompanying drawing of a part for constitution instruction shows embodiments of the invention, and even
It is used for the principle for explaining the present invention together with its explanation.
Fig. 1 is a kind of flow chart for the method that track of vehicle prediction is carried out based on big data in the embodiment of the present invention 1；
Fig. 2 is a kind of flow chart for the method that track of vehicle prediction is carried out based on big data in the embodiment of the present invention 2.
Embodiment
The various exemplary embodiments of the present invention are described in detail now with reference to accompanying drawing.It should be noted that：Unless have in addition
Body illustrates that the unlimited system of part and the positioned opposite of step, numerical expression and the numerical value otherwise illustrated in these embodiments is originally
The scope of invention.
The description only actually at least one exemplary embodiment is illustrative to be never used as to the present invention below
And its application or any restrictions that use.
It may be not discussed in detail for technology, method and apparatus known to person of ordinary skill in the relevant, but suitable
In the case of, the technology, method and apparatus should be considered as part for specification.
In shown here and discussion all examples, any occurrence should be construed as merely exemplary, without
It is as limitation.Therefore, other examples of exemplary embodiment can have different values.
It should be noted that：Similar label and letter represents similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined, then it need not be further discussed in subsequent accompanying drawing in individual accompanying drawing.
Embodiment 1
Fig. 1 is a kind of flow chart for the method that track of vehicle prediction is carried out based on big data in the embodiment of the present invention 1, is such as schemed
Shown in 1, a kind of method that track of vehicle prediction is carried out based on big data provided by the invention, including：
S101:Determine the driving path of target vehicle.
Specifically, it needs to be determined that to carry out the target vehicle of trajectory predictions, by camera, take pictures, the side such as manual record
Formula obtains the driving path that target vehicle has been passed by, and driving path includes：Current the site C and the first history bit of target vehicle
Point O1, wherein, the first history site O1For a upper site adjacent with current site C, that is to say, that target vehicle is from first
History site O1Current site C is driven to, the driving path for being now to pass by based on target vehicle predicts that it may be walked
Next site, driving path can include multiple history sites, only choose the first history site in the present embodiment, can be with
Speed during computing is improved, but it is also possible to choose multiple history sites, improves the degree of accuracy of prediction.
S102:Any site is chosen as first object site F1。
Specifically, first object site F1It is to speculate next site that the target vehicle may drive to, that is, guesses mesh
Mark vehicle drives to first object site F from current site C1, method provided by the invention is it is expected that drive to first object
Position F1Probability and the time, first object site F1Can be any site, that is to say, that can be the first history site O1,
It can be current site C, that is, predict that target vehicle returns to the first history site O from current site C1Or rest on current site
C is not moved.First object site F1, with current site C or the first history site O1Can also be different.
S103:History vehicle data is obtained, extracts basic data and correction data.
Specifically, history vehicle data includes the vehicle data of multiple vehicles in preset time period, vehicle data includes one
Driving path of the individual vehicle in preset time period, driving path include the site of multiple acquisition times, from history vehicle data
Middle extraction：Include the first history site O in driving path successively1, current site C and first with reference to site A vehicle data, make
Based on data, wherein：First with reference to site A is any site, then is extracted from history vehicle data：In driving path successively
Including the first history site O1, current site C and first object site F1Vehicle data, data as a comparison.History vehicle number
According to data volume it is larger, in order to improve calculating speed, it is 1 year that can set preset time period, statistics this section till now the year before
In time, in all driving paths, including：First history site O1With current site C vehicle data, then therefrom acquisition traveling
Include first object site F simultaneously in path1Vehicle data, data, and including the first history site O as a comparison1, when
Data based on the references of preceding site C and first site A vehicle data.It should be noted that a vehicle, when default
In, repeatedly run over the first history site O1, current site C and first object site F1, or repeatedly run over the first history
Site O1, current site C and first with reference to site A, should now be considered as multiple vehicle datas, and can not be because of being a vehicle
In the data of different time, a vehicle data is treated as, i.e., by the site specified several times, is just designated as several vehicle datas.
S104:Correction data and basic data are grouped.
By basic data, according to acquisition time from closely to being far ranked up, obtaining in basic data preceding 20% vehicle number
According to, as first foundation group, obtain in basic data close to first foundation group rear 30% vehicle data as the second base set,
Last 50% vehicle data in basic data is obtained, as the 3rd base set.
By correction data, according to acquisition time from closely to being far ranked up, obtaining in correction data preceding 20% vehicle number
According to, as the first contrast groups, obtain in correction data close to the first contrast groups rear 30% vehicle data as the second contrast groups,
Last 50% vehicle data in correction data is obtained, as the 3rd contrast groups.
Specifically, according to acquisition time before closely to being far ranked up, just refer to, the data gathered in the recent period are placed on, most
The data early gathered are put behind, such as acquisition time section is the January to December of same year, then before the data of collection in December are placed on
Face, January, the data of collection were placed on finally.
S105:Calculate the first probability.
Target vehicle is calculated using following equation and passes through first object site F1The first probability P1X：
P1X=(U11X*5+U12X*3+U13X*2)/(U11*5+U12*3+U13*2)
Wherein：U11XIt is the number of vehicle data in the first contrast groups, U12XIt is the number of vehicle data in the second contrast groups,
U13XIt is the number of vehicle data in the 3rd contrast groups, U11It is the number of vehicle data in first foundation group, U12It is the second basis
The number of vehicle data, U in group13It is the number of vehicle data in the 3rd base set.
Specifically：The driving path of people's driving vehicle and time are closely related, because the original such as urban construction, road change
Cause, even destination is identical, the path within the different time selected by people is also different, so most reference price
The data of value are the data gathered in the recent period, so the maximum of the weight setting of the first contrast groups, the data of acquisition time more rearward
Weighted value is smaller, passes through first object site F so as to estimate target vehicle1Probability, by repeating the above steps, it becomes possible to
The driving path that target vehicle is most possibly chosen is calculated, optionally, chooses first object site F1When, can be referring initially to
The driving path of one base set, wherein most possible site is chosen as first object site F1。
S106:Calculate very first time T1X。
Target vehicle is calculated from current site C to first object site F using following equation1Very first time T used1X：
T1X=(T11X*5+T12X*3+T13X*2)/(T11*5+T12*3+T13*2)
Wherein：T11XBe in the first contrast groups each vehicle from current site C to first object site F1The average used time, T12X
Be in the second contrast groups each vehicle from current site C to first object site F1The average used time, T13XIt is each in the 3rd contrast groups
Vehicle is from current site C to first object site F1The average used time, T11Be in first foundation group each vehicle from current site C to
First reference site A average used time, T12It is each vehicle putting down from current site C to first references site A in the second base set
Equal used time, T13It is average used time of each vehicle from current site C to first with reference to site A in the 3rd base set.
Specifically, very first time T1XIt is to reach first object site F from current site C1Time used, when calculating first
Between T1XWhen set weight consideration with calculate the first probability P1XWhen consideration it is identical, in a practical situation, because target vehicle is past
Toward constantly moving, so the first probability P calculated1XIt is effective, in the prior art, majority does not provide target
Vehicle movement is predicted when the time in next site, or only by the mode of distance divided by target vehicle average speed,
And in actual conditions, each local road conditions are different, and the prediction carried out with the mode of distance divided by speed is present very
Large deviation, specific vehicle data is combined in of the invention, and different weights is assigned according to different acquisition times, given
Most possible very first time T1X, improve prediction accuracy.
Embodiment 2
The improvement that embodiment 2 is carried out on the basis of embodiment 1, all explanations done in embodiment 1 are also applied for
Embodiment 2, it is to be based on current site C and the first history site O in embodiment 11The next site that will pass through is predicted, is implemented
Example 2 is to be based on current site C and the first history site O1Predict lower two sites that will pass through.
Fig. 2 is a kind of flow chart for the method that track of vehicle prediction is carried out based on big data in the embodiment of the present invention 2, is such as schemed
Shown in 2, a kind of method that track of vehicle prediction is carried out based on big data provided by the invention, including：
S201:Determine the driving path of target vehicle.
S202:Any site is chosen as first object site F1, any site is chosen as the second target site F2。
Specifically, first object site F1With the second target site F2Speculate that the target vehicle may drive to successively
Lower two sites, that is, guess that target vehicle drives to first object site F from current site C1, then drive to the second target site
F2, first object site F1With the second target site F2Can be any site, that is to say, that both can with it is identical can not also
Together.
S203:History vehicle data is obtained, extracts master data and contrasting data.
Extracted from history vehicle data：Include the first history site O in driving path successively1, current site C, first
Vehicle data with reference to site A and second with reference to site B, as master data, wherein：Second with reference to site B is any site.
Extracted from history vehicle data：Include the first history site O in driving path successively1, current site C, first
Target site F1With the second target site F2Vehicle data, as contrasting data.
S204:Master data and contrasting data are grouped.
By master data, according to acquisition time from closely to being far ranked up, obtaining in master data preceding 20% vehicle number
According to, as first basic group, obtain in master data close to the first rear 30% vehicle data organized substantially, it is basic as second
Group, last 50% vehicle data in master data is obtained, as the 3rd basic group.
By contrasting data, according to acquisition time from closely to being far ranked up, obtaining in contrasting data preceding 20% vehicle number
According to, as the first control group, obtain in contrasting data close to the first control group rear 30% vehicle data as the second control group,
Last 50% vehicle data in contrasting data is obtained, as the 3rd control group.
S205:Calculate the second probability P2X。
Target vehicle is calculated using following equation and passes through first object site F1With the second target site F2The second probability
P2X：
P2X=(U21X*5+U22X*3+U23X*2)/(U21*5+U22*3+U23*2)
Wherein：U21XIt is the number of vehicle data in the first control group, U22XIt is the number of vehicle data in the second control group,
U23XIt is the number of vehicle data in the 3rd control group, U21It is the number of vehicle data in first basic group, U22It is second basic
The number of vehicle data, U in group23It is the number of vehicle data in the 3rd basic group.
S206:Calculate the second time T2X。
Target vehicle is calculated using following equation and passes through first object site F from current site C1With the second target site F2
Second time T used2X：
T2X=(T21X*5+T22X*3+T23X*2)/(T21*5+T22*3+T23*2)
Wherein：T21XIt is that each vehicle passes through first object site F from current site C in the first control group1With the second target position
Point F2The average used time, T12XIt is that each vehicle passes through first object site F from current site C in the second control group1With the second target
Site F2The average used time, T23XIt is that each vehicle passes through first object site F from current site C in the 3rd control group1With the second mesh
Mark point F2The average used time, T21It is that each vehicle is joined from current site C by first with reference to site A and second in first basic group
According to site B average used time, T12It is that each vehicle is joined from current site C by first with reference to site A and second in second basic group
According to site B average used time, T13It is that each vehicle is joined from current site C by first with reference to site A and second in the 3rd basic group
According to the site B average used time.
Specifically, in the present embodiment, predict and pass sequentially through first object site F1With the second target site F2Second
Probability P2XAnd the second time T2X, the second time T2XIt is to pass through first object site F from current site C1Reach the second target position
Point F2It is time-consuming, optionally, the method in embodiment 1 can also be used, first predict from current site C and reach first object site
F1Probability and time-consuming, then predict from first object site F1To the second target site F2Probability and time-consuming, so as to obtain second
Probability P2XAnd the second time T2X, compared to the method predicted in two steps, the method provided in embodiment 2 is calculated by a step,
Directly obtain and pass sequentially through first object site F1With the second target site F2The second probability P2XAnd the second time T2XIt is general
Rate, it is time-consuming so as to reduce, cross and improve calculating speed, by choosing the first different site F1With the second site F2And respectively
2nd P corresponding to calculating2XIt is known that the driving path that target vehicle is most possible.
Further, in some optional embodiments, any site is being chosen as first object site F1Before, also wrap
Include：Obtained from the driving path of target vehicle, with the first history site O1An adjacent upper site, as the second history site
O2；
Now, in the driving path of basic data and correction data, all include successively：Second history site O2, first go through
History site O1With current site C.
Further, in some optional embodiments, any site is being chosen as first object site F1Before, also
Including：Obtained from the driving path of target vehicle, with the first history site O1An adjacent upper site, as the second history bit
Point O2；
Now, in the driving path of basic data, the first master data, correction data and the first contrasting data, all successively
Including：Second history site O2, the first history site O1With current site C.
All it is by current site C and the first history site O specifically, in embodiment 1 and embodiment 21To predict down
One or two site, in order to improve precision of prediction, the second history site O can be based on2, the first history site O1And present bit
Point C is predicted, optionally, can be with the case where having plenty of time so as to improve the reliability of result of calculation and the degree of accuracy
More history sites are chosen, further improve the reliability of result of calculation.
Further, in some alternative embodiments, obtain history vehicle data, further for：Obtain unstructured car
Unstructured vehicle data are converted into structured vehicle data, obtain all structured vehicle data as history by data
Vehicle data.
Specifically, vehicle data may be from various aspects, there are data, the computers such as the electric police for being easier to access, bayonet socket can
Analysis is directly read, also there is the video information from society's monitoring, other relevant informations such as confession of witness.Single platform
Do not possess the ability for collecting and integrating different channel informations.The information such as video, picture on vehicle, it is unstructured vehicle number
According to, be not easy to computer Direct Analysis with use, unstructured vehicle data are converted into structuring car by architectural system
Data, can solve the problems, such as that unstructured vehicle data are not readily used for computer Direct Analysis with using, using this method
System, by the unstructured vehicle data of acquisition transfer to unstructured storage system uniformly to store and provide access service, knot
After unstructured vehicle data are converted into structured vehicle data by structure system, be able to can further it locate with resource description information
Storage is into the distributed data base system towards row after reason.
Further, in some alternative embodiments, structured vehicle data storage is in the distributed data base towards row
In system, Database Systems externally provide unified interface；Unstructured vehicle data are converted into structured vehicle data, entered
One step is：The information of unstructured vehicle data is extracted, information is subjected to typing according to the form of structure.
Specifically, being distributed data base one unified data-interface of external offer towards row, solves information of vehicles
Form is inconsistent, and source is inconsistent, is not easy to obtain, and the problem of can not analyzing comprehensively, and uses the distributed data towards row
The vehicle data of library storage structuring, can solve conventional relationship type database and be unable to vehicle data exponential increase and a large amount of
The problem of the problem of data storage, and system are not easy dilatation, and fault-tolerant redundancy ability is bad.
Optionally, non-institutional vehicle data, including：Video, picture and voice；Extract the letter of unstructured vehicle data
Breath, including：Judge the classification of unstructured vehicle data；When non-structural vehicle data is video, the frame extracted in video is drawn
Face, using the information in image identification method getting frame picture；When non-structural vehicle data is picture, using picture recognition side
Information in method getting frame picture；When non-structural vehicle data is voice, voice is converted into by word using speech recognition system,
Obtain the information in word.
Specifically：General bayonet socket, electricity police etc. dispose by planning big companies of government department according to certain Specification Design, can
Vehicle data is directly read by data address or timing inquires about its database and obtains vehicle data, then the online money that will be read
The temp directory that source storage is specified to system, and resource associated description information is obtained automatically.The acquisitions such as social monitoring device regard
Frequently, the resource such as picture does not access network typically, can not directly read, and can only be obtained by offline mode, can be directly by correlation
Resource is copied directly to system and specifies temp directory, and the relevant informations such as time, the position of the data, system meeting are filled in system
Handled respectively for the type of resource, using image recognition technology or speech recognition technology, obtain information therein.
Further, in some optional embodiments, vehicle data, in addition to：The number-plate number.
The invention provides it is a kind of based on big data carry out track of vehicle prediction method, to using this method personnel without
Skill requirement, solves the problems, such as high to staff's skill requirement in the prior art, and can predicted using this method
While vehicle running path, prediction vehicle drives to the time in the path, solves and is difficult to predicted time in the prior art
Problem, use unified interface in this method, unstructured data be uniformly converted into structural data, make it is each it is different come
Source, the vehicle data of form conveniently access analysis system, reduce the complexity of data access, improve what data can access
Scope.
Although some specific embodiments of the present invention are described in detail by example, the skill of this area
Art personnel it should be understood that example above merely to illustrating, the scope being not intended to be limiting of the invention.The skill of this area
Art personnel to above example it should be understood that can modify without departing from the scope and spirit of the present invention.This hair
Bright scope is defined by the following claims.
Claims (8)
- A kind of 1. method that track of vehicle prediction is carried out based on big data, it is characterised in that including：The driving path of target vehicle is determined, the driving path includes：The current site C of the target vehicle and the first history Site O1, wherein, the first history site O1For a upper site adjacent with the current site C；Any site is chosen as first object site F1；History vehicle data is obtained, wherein, the history vehicle data includes the vehicle data of multiple vehicles in preset time period, The vehicle data includes a vehicle driving path in the preset time period, the driving path, including：Multiple institutes Rheme point and the acquisition time in the site；Extracted from the history vehicle data：Include the first history site O in the driving path successively1, the current site C and first with reference to site A institute State vehicle data, based on data, wherein：Described first with reference to site A is any site,Include the first history site O in the driving path successively1, the current site C and first object site F1 The vehicle data, data as a comparison；By the basic data, according to the acquisition time from closely to being far ranked up, obtaining preceding 20% in the basic data The vehicle data, as first foundation group, obtain in the basic data close to rear 30% institute of the first foundation group Vehicle data is stated as the second base set, last 50% vehicle data in the basic data is obtained, as the 3rd basis Group；By the correction data, according to the acquisition time from closely to being far ranked up, obtaining preceding 20% in the correction data The vehicle data, as the first contrast groups, obtain in the correction data close to rear 30% institute of first contrast groups Vehicle data is stated as the second contrast groups, last 50% vehicle data in the correction data is obtained, as the 3rd contrast Group；The target vehicle is calculated using following equation and passes through the first object site F1The first probability P1X, and from present bit Point C to the first object site F1Very first time T used1X：P1X=(U11X*5+U12X*3+U13X*2)/(U11*5+U12*3+U13*2)T1X=(T11X*5+T12X*3+T13X*2)/(T11*5+T12*3+T13*2)Wherein：U11XIt is the number of vehicle data described in first contrast groups, U12XIt is car described in second contrast groups The number of data, U13XIt is the number of vehicle data described in the 3rd contrast groups, U11It is described in the first foundation group The number of vehicle data, U12It is the number of vehicle data described in second base set, U13It is institute in the 3rd base set State the number of vehicle data, T11XBe in first contrast groups each vehicle from the current site C to the first object site F1The average used time, T12XBe in second contrast groups each vehicle from the current site C to the first object site F1's Average used time, T13XBe in the 3rd contrast groups each vehicle from the current site C to the first object site F1Be averaged Used time, T11It is average used time of each vehicle from the current site C to described first with reference to site A in the first foundation group, T12Be in second base set each vehicle from the current site C to described first with reference to site A average used time, T13It is Average used time of each vehicle from the current site C to described first with reference to site A in 3rd base set.
- 2. a kind of method that track of vehicle prediction is carried out based on big data according to claim 1, it is characterised in that also wrap Include：Any site is chosen as the second target site F2；Extracted from the history vehicle data：Include the first history site O in the driving path successively1, the current site C, described first with reference to site A and The second reference site B vehicle data, as master data, wherein：Described second with reference to site B is any site,Include the first history site O in the driving path successively1, current site C, the first object site F1 With the second target site F2Vehicle data, as contrasting data；By the master data, according to the acquisition time from closely to being far ranked up, obtaining preceding 20% in the master data The vehicle data, as first basic group, obtain in the master data close to the described first rear 30% institute organized substantially Vehicle data is stated, as second basic group, obtains last 50% vehicle data in the master data, it is basic as the 3rd Group；By the contrasting data, according to the acquisition time from closely to being far ranked up, obtaining preceding 20% in the contrasting data The vehicle data, as the first control group, obtain in the contrasting data close to rear 30% institute of first control group Vehicle data is stated as the second control group, last 50% vehicle data in the contrasting data is obtained, as the 3rd control Group；The target vehicle is calculated using following equation and passes through the first object site F1With the second target site F2 Two probability Ps2X, and from the current site C pass through the first object site F1With the second target site F2Used Second time T2X：P2X=(U21X*5+U22X*3+U23X*2)/(U21*5+U22*3+U23*2)T2X=(T21X*5+T22X*3+T23X*2)/(T21*5+T22*3+T23*2)Wherein：U21XIt is the number of vehicle data described in first control group, U22XIt is car described in second control group The number of data, U23XIt is the number of vehicle data described in the 3rd control group, U21It is described in described first basic group The number of vehicle data, U22It is the number of vehicle data described in described second basic group, U23It is institute in described 3rd basic group State the number of vehicle data, T21XIt is that each vehicle passes through the first object position from the current site C in first control group Point F1With the second target site F2The average used time, T12XBe in second control group each vehicle from the current site C Pass through the first object site F1With the second target site F2The average used time, T23XIt is each car in the 3rd control group Pass through the first object site F from the current site C1With the second target site F2The average used time, T21It is institute State each vehicle in first basic group and pass through the described first reference site A's and the second reference site B from the current site C Average used time, T12Be in described second basic group each vehicle from the current site C by described first with reference to site A and described Second reference site B average used time, T13Be in described 3rd basic group each vehicle from the current site C by described first The average used time with reference to site A and described second with reference to site B.
- 3. a kind of method that track of vehicle prediction is carried out based on big data according to claim 1, it is characterised in that selecting Any site is taken as first object site F1Before, in addition to：Obtained from the driving path of the target vehicle, with The first history site O1An adjacent upper site, as the second history site O2；Now, in the driving path of the basic data and the correction data, all include successively：Second history bit Point O2, the first history site O1With the current site C.
- 4. a kind of method that track of vehicle prediction is carried out based on big data according to claim 2, it is characterised in that selecting Any site is taken as first object site F1Before, in addition to：Obtained from the driving path of the target vehicle, with The first history site O1An adjacent upper site, as the second history site O2；Now, the row of the basic data, first master data, the correction data and first contrasting data Sail in path, all include successively：The second history site O2, the first history site O1With the current site C.
- 5. according to any a kind of described methods that track of vehicle prediction is carried out based on big data of claim 1-4, its feature exists In, it is described acquisition history vehicle data, further for：Obtain unstructured vehicle data；The unstructured vehicle data are converted into structured vehicle data；All structured vehicle data are obtained as the history vehicle data.
- 6. a kind of method that track of vehicle prediction is carried out based on big data according to claim 5, it is characterised in that described For structured vehicle data storage in the distributed data base system towards row, the Database Systems externally provide unified connect Mouthful；It is described that the unstructured vehicle data are converted into structured vehicle data, further for：The information of the unstructured vehicle data is extracted, described information is subjected to typing according to the form of the structure.
- 7. a kind of method that track of vehicle prediction is carried out based on big data according to claim 6, it is characterised in that described Non- institutional vehicle data, including：Video, picture and voice；The information of the extraction unstructured vehicle data, including：Judge the classification of the unstructured vehicle data；When the non-structural vehicle data is the video, the frame picture in the video is extracted, using image identification method Obtain the information in the frame picture；When the non-structural vehicle data is the picture, the letter in the frame picture is obtained using the image identification method Breath；When non-structural vehicle data is voice, the voice is converted into by word using speech recognition system, obtains the word In information.
- 8. according to a kind of method that track of vehicle prediction is carried out based on big data described in claim 1, it is characterised in that the car Data, in addition to：The number-plate number.
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