CN110489400A - A kind of realization people's vehicle acquisition data quasi real time associated algorithm model - Google Patents
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Abstract
The present invention proposes that a kind of realization people's vehicle acquires data quasi real time associated algorithm model.It includes several front-end collection equipment, server, disk array, big data component, database etc..Wherein front-end collection equipment is mainly used for acquiring the information of license plate, face and associated electronic device and records collecting location and acquisition time formation track data and report, server combination big data component cleans the track data reported, be put in storage and algorithm model calculates, disk array is used to store the track data of storage, big data component includes: Hadoop, Flume, Kafka, ElasticSearch etc., and database MySQL is used to store the relevant information of front-end collection equipment.Algorithm model various dimensions of the invention consider that reality factor calculates the associated matching degree of people's vehicle, adapt to reality scene and environment, and the accurate people's vehicle of finding out of energy is associated with and provides with reference to matching degree, provide important support for case investigation.
Description
Technical field
The present invention relates to terminal acquisition fields more particularly to a kind of realization people's vehicle to acquire data quasi real time associated calculation
Method model.
Technical background
The association of people's vehicle, which investigates and prosecute to case, provides important technical support.People in the present invention refers mainly to hand-hold electronic equipments
Identification code, is also possible to facial image, and vehicle refers to license plate number.Equipment, which is acquired, by various terminals acquires space-time trajectory data,
Carrying out space-time analysis excavation to these space-time trajectory datas accurate can obtain people Che Guanlian and association matching degree.
Because of various practical reasons, electronic device information acquire equipment acquisition rate it is limited, improve each electronic equipment with
The quasi real time association accuracy of license plate needs various dimensions to consider to calculate.
The dimension of consideration mainly has:
(1) significance level in time and space is different.It is different for the closeness of different time personnel, when low-density
People's vehicle co-occurrence is more credible, and spatially there is also the differences of density of personnel and flow of personnel speed, and the lower flowing of density of personnel is more
Slow place, people's vehicle co-occurrence is more credible, and corresponding weight is also higher.
(2) external concern is different with ownership place weight.Certain electronic equipments have ground domain identifier, if the vehicle of identical region
Board and electronic equipment co-occurrence, confidence level is higher, can increase its weight.
(3) it is limited by acquisition rate, there may be individual acquisition floor drains to adopt in the period of concern, at this moment considers that history is closed
Connection supplements real time correlation.Historical context is by an accumulative co-occurrence number of program record and according to the same related compounds
The co-occurrence number for being associated object with other calculates confidence level.
(4) consider reality, co-occurrence number reaches certain value, that is, has higher confidence level, avoids people and Che same
Time locus points difference is larger and the lesser situation of co-occurrence accounting causes matching degree lower；
(5) consider that there are the unreachable situations in track further to screen to result for people's wheel paths.
Summary of the invention
To achieve the goals above, technical solution provided by the present application is as follows:
A variety of front end data acquisition equipments are mainly used for acquiring the information and record of license plate, face and associated electronic device
Collecting location and acquisition time form track data and report.
Server and big data component clean the track data reported, be put in storage and algorithm model calculates.Big data
Component includes: Hadoop, Flume, Kafka, ElasticSearch etc..
MySQL database is used to store the relevant information of front-end collection equipment.
Disk array is used to store the track data of storage.
Compared with prior art, the invention has the following beneficial effects: algorithm model various dimensions to consider that reality factor calculates
The associated matching degree of people's vehicle adapts to reality scene and environment, and the accurate people's vehicle of finding out of energy is associated with and provides with reference to matching degree, is
Case investigation provides important support.
Detailed description of the invention
Fig. 1 is system diagram according to the present invention；
Fig. 2 is algorithm model flow chart of the invention.
Specific embodiment
With reference to the accompanying drawings and detailed description, technical solution of the present invention is described in detail.
Technical solution of the present invention mainly consists of two parts: front end data acquisition and algorithm model.
Front end data acquisition is to acquire equipment and electronic information by being mounted on the camera image at each main traffic crossing
Acquire devices collect data and reported data center.When data information includes collecting location (place) and the acquisition of collected target
Between and correlation attribute information.Data flow: front-end collection equipment -- > Flume-- > Kafka-- > ElasticSearch.
The emphasis of the invention is in algorithm model, below with (other similar) association of certain electron-like acquisition keyword keyword
For license plate keyword, detailed algorithm model flow is introduced.
(1) ElasticSearch is passed through according to keyword and time started stamp (sTime) and ending time stamp (eTime)
Track (removal place and time all identical data) are inquired, for the place of these tracing points, if set without license plate acquisition
It is standby then remove corresponding tracing point, our available effective track points (trajecotryLen), in effective tracing point
Place, which is extracted, obtains a digit (sitecodeCount) with duplicate removal；
(2) vehicle that the tracing point (sitecode#time) at the appointed time poor (timeDiff) inquired in (1) respectively occurs
Board, when such as inquiring license plate and its acquisition that place sitecode occurs between time-timeDiff to time+timeDiff
Between；
(3) data obtained in (2) are converted, the corresponding co-occurrence tracing point of license plate of each potential colleague, co-occurrence rail are obtained
Mark points are co-occurrence number (sharetotal), extract available co-occurrence after duplicate removal to the place in co-occurrence tracing point
Point digit (sharespacesize).
It is as follows to be associated with matching degree benchmark:
Based on sitecodeCount, one matching degree loss factor facterParameter (being defaulted as 0.7) is set, such as:
Facter=1- (facterParameter/Math.pow (2, sitecodeCount-1))
Wherein sitecodeCount -1 power that Math.pow (2, sitecodeCount-1) is 2, when
SitecodeCount is smaller, and matching degree loss is bigger.
The calculation of comprehensive matching degree is as follows:
Samerate=(w1*sharetotal/trajecotryLen+w2*sharespacesize/
sitecodeCount)*facter
Wherein w1 and w2 is respectively co-occurrence number weight and co-occurrence place number weight, and default assigns w1=0.4, w2=0.6,
It can be adjusted according to real data situation.
The general frame of the invention algorithm model above, introduce one by one below several dimensions for considering in algorithm model because
Element:
1) consider that the significance level of time and place are different (weight coefficient is configurable).
Time co-occurrence weight is embodied in sharetotal, divides time into four periods: 7-9,10-16,17-21,
22-6, for the time of each tracing point of keyword, the corresponding co-occurrence weight of different periods is different, wherein 7-9 and
17-21 co-occurrence weight is 1；10-16 co-occurrence weight is 2；22-6 co-occurrence weight is 3, to all co-occurrence situation and different co-occurrences
Weight sums to obtain sharetotal.
Place co-occurrence weight is embodied in sharespacesize, for each tracing point of keyword to be associated
Place significance level is also classified into three grades by place, general/important/extremely important, corresponding co-occurrence place weight difference
It is 1/2/3, all co-occurrence situations and different places co-occurrence weight is summed to obtain sharespacesize.
2) increase matching degree when considering two class keywords with ownership place for keyword and license plate:
The ownership place paid close attention to by setting keyword, is divided into three grades here, general/important/extremely important, different
Grade controls different facterParameter loss factors, its more important corresponding loss factor is smaller, as:
FacterParameter=0.7；Important facterParameter=0.5；Extremely important facterParameter=0.3.
3) consider that (historical context needs another program record co-occurrence number and calculates confidence the case where historical context
Degree), if including the biggish license plate of historical context confidence level in the license plate that association comes out, by the historical context confidence level
RelSamerate (if there is no historical context, then the value is 0) to be added in above-mentioned matching degree calculating, specific as follows:
SamerateR=samerate* (1-relSamerate)+relSamerate*Math.max (samerate,
relSamerate)
4) consider co-occurrence situation, i.e., if when co-occurrence number/co-occurrence place number reaches certain numerical value, association has been compared
It is accurate, at this moment wish that whole matching degree is larger, the specific co-occurrence factor (shareFacter) calculation method is as follows:
ShareFacterT=1- (facterParameter/Math.pow (1.3, shareTotal-1))
ShareFacterS=1- (facterParameter/Math.pow (1.3, shareSpacesize-1))
ShareFacter=w1*shareFacterT+w2*shareFacterS
Wherein facterParameter, w1 are with w2 as above-mentioned value.
Then co-occurrence factor ladder is given according to co-occurrence number (sharetotal) and co-occurrence place number (sharespacesize)
Degree assigns weight (meanFacter):
Mean=(shareTotal+shareSpacesize)/2
If mean < 3, meanFacter=0.5；If 3≤mean < 5, meanFacter=0.6；If 5≤
Mean < 8, meanFacter=0.7；If 8≤mean < 12, meanFacter=0.8；If mean > 12, meanFacter
=0.9
Final matching degree are as follows: (1-meanFacter) * samerateR+meanFacter*shareFacter
5) postsearch screening interface is provided for association results, if imsi1 is with plate1 is obtained, interface can detect imsi1
It whether there is the inaccessible situation in tracing point path between [sTime, eTime] with plate1.
The above, the only specific embodiment of the embodiment of the present application, but the protection scope of the embodiment of the present application is not
It is confined to this, anyone skilled in the art can think easily in the technical scope that the embodiment of the present application discloses
To change or replacement, should all cover within the protection scope of the embodiment of the present application.Therefore, the protection scope of the embodiment of the present application
It should be based on the protection scope of the described claims.
Claims (2)
1. a kind of realization people's vehicle acquires data quasi real time associated algorithm model system characterized by comprising several front end numbers
According to acquisition equipment, server, disk array, big data component, MySQL database；Wherein front-end collection equipment is mainly used for adopting
Collect the information of license plate, face and associated electronic device and records collecting location and acquisition time formation track data and report；Clothes
Business device and big data component the track data reported is cleaned, be put in storage and algorithm model calculate；Disk array is for storing
The track data of storage；MySQL database is used to store the relevant information of front-end collection equipment.
2. a kind of real time correlation algorithm model, which is characterized in that dug using different type track data source by multi dimensional analysis
The incidence relation and calculating matching degree between it are dug, here either " people " association " vehicle ", is also possible to " vehicle " association " people ",
Except this be also applied to it is interrelated between other different acquisition features.
For track trajecotry of some electronic equipment within sTime to the eTime time, tracing point length is
TrajecotryLen is sitecodeCount to number after the duplicate removal of tracing point place, when looking for its front and back by each tracing point
Between the license plate that occurs of difference timeDiff, it is total to calculate license plate that each co-occurrence is crossed the license plate as crossed with this electronic equipment co-occurrence
The place duplicate removal number sharespacesize of existing number sharetotal and co-occurrence, is arranged a matching degree loss factor
FacterParameter is calculated match degree factor (facterParameter is defaulted as 0.7): facter=1-
(facterParameter/Math.pow(2,sitecodeCount-1))
Wherein sitecodeCount -1 power that Math.pow (2, sitecodeCount-1) is 2, when sitecodeCount is got over
Small, matching degree loss is bigger.
The calculation of comprehensive matching degree is as follows:
Samerate=(w1*sharetotal/trajecotryLen+w2*sharespacesize/sitecodeCo unt) *
facter
Wherein w1 and w2 is respectively co-occurrence number weight and co-occurrence place number weight, and default assigns w1=0.4, w2=0.6, can be with
It is adjusted according to real data situation.
Furthermore consider the time and space significance level it is different, by control reset section sharetotal and
The size of sharespacesize achievees the purpose that adjust matching degree samerate；External concern ownership place weight is different, leads to
It crosses control and resets section facterParameter size, achieve the purpose that adjust matching degree samerate；Consider that historical context is made
For supplement, historical context confidence level is assigned to weight and the final matching degree of samerate fusion calculation；Consider real co-occurrence number
Reaching certain value just has higher confidence level, and gradient weight is arranged by control co-occurrence number and co-occurrence place number and controls newest matching
Degree；Consider the unreachable further screening of people's wheel paths, compares whether two tracks of people's vehicle within a specified time whether there is space
Unreachable situation, if it does, it may be considered that the association is insincere.
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