CN106899668B - Information Push Service processing method in car networking - Google Patents

Information Push Service processing method in car networking Download PDF

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CN106899668B
CN106899668B CN201710099060.7A CN201710099060A CN106899668B CN 106899668 B CN106899668 B CN 106899668B CN 201710099060 A CN201710099060 A CN 201710099060A CN 106899668 B CN106899668 B CN 106899668B
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individual
vehicle
car networking
degree
information
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CN106899668A (en
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黄震华
程久军
孙剑
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Tongji University
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Tongji University
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/50Network services
    • H04L67/55Push-based network services
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/12Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The invention proposes Information Push Service processing methods in a kind of novel car networking.In the art, it is first depending on vehicle connection individual attribute feature and car networking is divided into several sub- car networkings, so that the car networking overall situation degree of correlation loss reduction of segmentation front and back;Then, the quantity based on idle server in system, optimization distributes and handles sub- car networking on these servers, thus the workload of balanced idle server;Individual is joined for each vehicle in car networking sub- on server, the highest part of its interest-degree is obtained and is pushed information, and in whole car networking, carries out global adaptation to information is pushed, pushes effect to reach optimal information.The present invention can significantly improve the accuracy of Information Push Service, diversity and efficiency in car networking.

Description

Information Push Service processing method in car networking
Technical field
The present invention relates to Information Push Service processing techniques in a kind of car networking, belong to intelligent transportation and intersect with information technology Field.
Background technique
In Baidupedia, alliance, Chinese Internet of Things school-run enterprise is about car networking (IoT:Internet of Vehicles) Is defined as: car networking is the huge Internet being made of information such as vehicle location, speed and routes.Pass through GPS, RFID, biography Devices, the vehicles such as sensor, camera image processing can complete the acquisition of itself environment and status information;Pass through internet skill Art, all vehicles can be by the various information Transmission Convergences of itself to central processing unit;By computer technology, these are a large amount of The information of vehicle can be analyzed and processed, to calculate the best route of different vehicle, report without delay road conditions and arrange letter The signal lamp period.
Information Push Service is a key technology of car networking, its main purpose is for each vehicle in car networking Join individual v, the upper interested information of v of other vehicles connection individual be pushed to v, v is enabled to share the information of other vehicle connection individuals, And it is not limited to the information oneself possessed.Here information includes diversified forms, can be music, video, text, picture Deng.
Currently, Information Push Service is mainly based upon the information advancing technique of car networking structure feature in car networking.The skill Art mainly utilizes the structure feature of the connection individual of vehicle in car networking, by calculate such as mutually cut-off jointly join individual, Jaccard and The Measure Indexes as similarity such as Adamic/Adar.Individual v is joined for each vehicle in car networking, we are a joining with vehicle The relevant vehicle connection individual collections of body v are set as R (v), in addition, if vehicle connection individual v is interested in information λ, interest-degree r (v, λ)=1, otherwise r (v, λ)=0.It is not difficult to find out that v and in R (v) different vehicles connection individual v ' between the degree of correlation and to certain information Interest-degree be different.The degree of correlation between v and v ' is calculated by Jaccard coefficient method, it may be assumed that
And the Interest Similarity between v and v ' can calculate the interested letter of vehicle connection individual by Jaccard coefficient method How many common information acquisition in the set of breath, it may be assumed that
Wherein N (v) and N (v ') respectively indicates information aggregate interested to vehicle connection individual v and v '.To which we can be with Different method for measuring similarity is combined together by different weight factors, is returned by fitting and determines weight factor Optimal value wt (v, v '), then the calculating as similarity between final vehicle connection individual, that is, indicate are as follows:
fsλ=∑v'∈R(v)r(v',λ)wt(v,v')。
Finally, will be by fsλAppropriate number of information is pushed to vehicle connection individual v by value sequence.
We have found that in existing car networking Information Push Service processing technique Information Push Service accuracy, more All there is deficiencies for sample and efficiency etc., to influence the quality of Information Push Service in car networking.
Summary of the invention
The purpose of the present invention is to overcome the deficiency in the prior art, proposes Information Push Service processing skill in a kind of car networking Art, workflow are mainly made of 4 steps:
1, car networking G, the number k for needing pushed information and traversal threshold value t is obtained from input terminal (when pushed information, to need The vehicle of access joins individual amount), and car networking is divided by d sub- car networking G based on vehicle connection individual attribute feature1,G2,…, Gd, so that the sub- car networking set { G after segmentation1,G2,…,GdAnd segmentation before car networking G between the global degree of correlation loss most It is small.
2, according to the idle server quantity s in system, by d obtained in step 1 sub- car networking G1,G2,…,GdPoint It is fitted in these idle servers, this s platform idle server starts the sub- car networking above parallel processing respectively later.
3, for car networking G on serverxEach vehicle in (1≤x≤d) joins individual v, obtains all not on v The set of information compositionThen, forIn each Pre-Evaluation information λ, calculate v to the interest-degree of λ.WhenIn The interest-degree of all Pre-Evaluation information calculates finish after, return the highest k information λ of interest-degree12,…,λk
4, individual v is joined for each vehicle in car networking G, obtains the vehicle directly related with v and join individual collections M, obtains simultaneously Vehicle connection individual collections R directly related with v in sub- car networking is taken, specified quantity l and v phase are then chosen in set M-R The vehicle of pass joins individual, joins individual for selected vehicle, obtains the highest k information of its interest-degree.When all selected vehicles join The acquisition of information of individual finishes, and is incorporated in part pushed information obtained in step 3, obtains the highest k letter of final interest-degree It ceases and sends vehicle connection individual v to.
The invention has the following advantages that
1, the present invention can effectively improve the diversity of car networking information push by the extreme saturation to vehicle connection individual.
2, the present invention can be mentioned significantly by the seamless combination to vehicle connection individual local message push and global information push The coverage rate of high car networking information push.
3, the present invention is based on the car networking segmentations of minimum global degree of correlation loss, and then parallelization handles the side of sub- car networking Formula can significantly reduce the time overhead of car networking information push.
Detailed description of the invention
Work flow diagram Fig. 1 of the invention
Fig. 2 calculates vehicle connection individual v to the process flow diagram of Pre-Evaluation information inf interest-degree
Specific embodiment
The invention proposes Information Push Service processing techniques in a kind of car networking, are first depending on vehicle connection individual attribute feature Car networking is divided into several sub- car networkings, so that the car networking overall situation degree of correlation loss reduction of segmentation front and back;Then, it is based on The quantity of idle server in system, optimization distributes and handles sub- car networking on these servers, thus balanced leisure service The workload of device;Individual is joined for each vehicle in car networking sub- on server, the highest part of its interest-degree is obtained and is pushed away It delivers letters breath, and in whole car networking, carries out global adaptation to information is pushed, push effect to reach optimal information. The present invention can significantly improve the accuracy of Information Push Service, diversity and efficiency in car networking.Workflow such as Fig. 1 institute Show:
Step 1, from input terminal obtain car networking G, need pushed information number k and traversal threshold value t (when pushed information, The vehicle connection individual amount for needing to access), and car networking is divided by d sub- car networking G based on vehicle connection individual attribute feature1, G2,…,Gd, so that the sub- car networking set { G after segmentation1,G2,…,GdAnd segmentation before car networking G between the global degree of correlation damage It loses minimum.
Step 2, according to the idle server quantity s in system, by d obtained in step 1 sub- car networking G1,G2,…, GdIt is assigned in these idle servers, this s platform idle server starts the sub- car networking above parallel processing respectively later.
Step 3, for car networking G on serverxEach vehicle in (1≤x≤d) joins individual v, obtains not on v The set of all information compositionsThen, forIn each Pre-Evaluation information λ, calculate v to the interest-degree of λ.WhenIn all Pre-Evaluation information interest-degree calculate and finish after, return to the highest k information λ of interest-degree12,…,λk
Step 4 joins individual v for each vehicle in car networking G, obtains the vehicle directly related with v and joins individual collections M, together When obtain the vehicle directly related with v in sub- car networking and join individual collections R, then chosen in set M-R specified quantity l and The relevant vehicle connection individual of v, joins individual for selected vehicle, obtains the highest k information of its interest-degree.When all selected vehicles The acquisition of information of connection individual finishes, and the comprehensive local pushed information obtained in step 3 obtains final interest-degree highest k Information simultaneously sends vehicle connection individual v to.
Each step is described in detail below:
One '
In step 1 of the invention, it includes three parts that vehicle, which joins individual attribute feature:
1) the statistics attributive character of vehicle connection individual, the statistics attributive character including car owner: age, gender, native place, duty Industry, previous graduate college, work unit, obtains the driving license time etc. at income;The statistics attributive character of vehicle: the brand of vehicle, model, vehicle Age, mileage number, number, maintenance frequency violating the regulations etc..
2) interest of vehicle connection individual likes attributive character, including information interested to car owner, pushed and gives other car owners Information, information format interested to subject categories, car owner interested to car owner etc..
3) the physical location attributive character of vehicle connection individual: including the city where vehicle, zone of action, postcode, average vehicle Speed, running time, even more accurate geographical location attributive character etc..
In addition, the car networking G in step 1 is indicated with graph structure, i.e. G=(V, E, W), wherein V is all vehicle connection in G The set of body composition, E are the set that correlativity forms between vehicle connection is individual, and W is the set of degree of correlation composition between vehicle connection individual, E In each correlativity there is a degree of correlation in W to be corresponding to it.Individual v is joined for any two vehicle in car networking G1 And v2If they have correlativity, i.e. (v1,v2) ∈ E, then their degree of correlation W (v1,v2) calculate as follows, If without correlativity, the degree of correlation 0:
Step 1-1, z attributive character a for joining individual relatedness computation for vehicle in car networking G is obtained1,a2,…,az, into And obtain corresponding with G | V | × z vehicle joins individual-attributive character matrix:
Wherein vi(aj) indicate that vehicle joins individual viIn j-th of attributive character ajOn value, 1≤i≤| V |, 1≤j≤z.
Step 1-2, individual v is joined for vehicle each in G, calculates its attributive character mean valueAnd by matrix VA calculates covariance matrix corresponding with it:
Step 1-3, vehicle is joined into individual v1Z attributive character value be organized into one-dimensional vector and its transposition respectively,Vehicle is joined into individual v2Z attributive character value point It is not organized into one-dimensional vector and its transposition,
Step 1-4, v is calculated1And v2Between the degree of correlation:
After the relatedness computation between vehicles all in G connection individual finishes, in order to which the overall situation for dividing front and back car networking is related Loss reduction is spent, we are based on following steps and G is divided into d sub- car networkings, and in the specific implementation, d takes in 10,15 and 20 One number:
(1, using k-means (k- mean value) clustering algorithm G is divided into d sub- car networking class IG={ G1,G2,…,Gd, And calculate the global degree of correlation of IG:
(2, following operation is repeated to sub- car networking class IGIt is secondary:
(2.1, copy IG a copy IC={ G1,G2,…,Gd};
(2.2, randomly select from ICTo sub- car networking, and to each pair of sub- car networking G being selectedτAnd GεIt executes such as Lower operation obtains new sub- car networking IC '={ G1’,G2’,…,Gd' }:
(2.2.1, obtain GτIn cause and G since car networking is dividedεBetween the degree of correlation lose nτA vehicle connection individualG is obtained simultaneouslyεIn cause and G since car networking is dividedτBetween the degree of correlation lose nεA vehicle connection individual
(2.2.2, in VτMiddle selection and GεIt is highest that middle vehicle joins the individual degree of correlationA vehicle connection individual, is denoted asMeanwhile in VεMiddle selection and GτIt is highest that middle vehicle joins the individual degree of correlationA vehicle connection individual, note For
(2.2.3, by GτMiddle vehicle connection individualWith GεMiddle vehicle connection individual It exchanges, obtains two new sub- car networking Gφ' and Gζ';
(2.3, more global degree of correlation gc (IC) and gc (IC '), if gc (IC) < gc (IC '), IG is updated to IC';
(3, return to final d sub- car networking IG={ G1,G2,…,Gd}。
Two,
In step 2 of the invention, in order to keep the workload of idle server balanced, we take the mode of being implemented as follows By d sub- car networking G1,G2,…,GdIt is assigned in s platform idle server: for every sub- car networking Gx(1≤x≤d) is obtained Its vehicle joins individual amount nx, and calculate the distribution average value upper boundThen s is divided into this d sub- car networkings Group, so that being grouped optimal measurementMinimum, wherein mθFor all sub- car networkings in θ platform idle server Vehicle joins individual amount summation.
Three,
In step 3 of the invention, vehicle connection individual v is calculated to process flow such as Fig. 2 of each Pre-Evaluation information λ interest-degree It is shown.
During processing, we obtain 3 inputs, i.e. vehicle connection individual v, Pre-Evaluation information λ and traversal threshold value first t;And initialize 4 intermediate variables, i.e. orderly information listIt traverses vehicle and joins individual ordered listTraverse number b=0 with And vehicle joins individual median v '=v.Then, it in the range of traversing threshold value t allows, obtains currently traversed vehicle and joins individual v ' Directly related vehicle joins individual collections R;And then it obtains the maximum vehicle of push ability value in R and joins individual pm, wherein pmPush ability Value cap (pm) it is represented by v ' and pmBetween the degree of correlation, i.e. cap (pm)=W (v ', pm);And by pmIt is inserted into traversal vehicle connection individual In ordered list H.Then, for pmOn the information aggregate S that is stored, obtain in S with the maximum information i of the λ interest degree of correlationm, Middle imWith the interest degree of correlation inco (i of λm, λ) and it may be expressed as:
Wherein e=2.72 is the bottom of natural logrithm,To possess i in the sub- car networking where v simultaneouslymWith the vehicle connection of λ Set composed by body,And inp,λRespectively known vehicle connection individual p is to imWith the interest-degree of λ,He is possessed for p The interest-degree average value of all information;And by imIt is inserted into orderly information list I.After reaching traversal threshold value t, we are last Based on ordered list I and H, vehicle connection individual v is calculated to the interest-degree in of λv,λ:
Four,
In step 4 of the invention, when choosing specified quantity l vehicle connection individuals relevant to v in set M-R, we Use functionImplement l, i.e.,And it is chosen in set M-R according to the degree of correlation size with v L vehicle joins individual v before choosing1,v2,…,vl.Then, individual v is joined for each vehicley(1≤y≤l), based on the interest in step 3 Calculation method is spent, the highest information λ of interest-degree is obtained1 (y)2 (y),…,λk (y).Finally, we merge step 3 and step 4 generates (l+1) k information obtain UI={ λ12,…,λk1 (1)2 (1),…,λk (1),…,λ1 (v)2 (v),…,λk (v), from The highest k information m_ λ of interest-degree is obtained in UI1,m_λ2,…,m_λkAnd send vehicle connection individual v to.

Claims (1)

1. Information Push Service processing method in a kind of car networking, which comprises the steps of:
Step 1 obtains car networking G, the number k for needing pushed information and traversal threshold value t from input terminal, and traversal threshold value t is push When information, the vehicle connection individual amount for needing to access, and car networking is divided by d sub- car networkings based on vehicle connection individual attribute feature G1,G2,…,Gd, so that the sub- car networking set { G after segmentation1,G2,…,GdRelated to the overall situation between the car networking G before segmentation Spend loss reduction;
Step 2, according to the idle server quantity s in system, by d obtained in step 1 sub- car networking G1,G2,…,GdPoint It is fitted in these idle servers, this s platform idle server starts the sub- car networking above parallel processing respectively later;
Step 3, for car networking G on serverxIn each vehicle join individual v, wherein 1≤x≤d, institute of the acquisition not on v The set being made of informationThen, forIn each Pre-Evaluation information λ, calculate v to the interest-degree of λ;When In all Pre-Evaluation information interest-degree calculate and finish after, return to the highest k information λ of interest-degree12,…,λk
Step 4 joins individual v for each vehicle in car networking G, obtains the vehicle directly related with v and joins individual collections M, obtains simultaneously Vehicle connection individual collections R directly related with v in sub- car networking is taken, specified quantity l and v phase are then chosen in set M-R The vehicle of pass joins individual, joins individual for selected vehicle, obtains the highest k information of its interest-degree;When all selected vehicles join The acquisition of information of individual finishes, the comprehensive local pushed information obtained in step 3, obtains the highest k letter of final interest-degree It ceases and sends vehicle connection individual v to;
In the step 1, car networking G is indicated with graph structure, i.e. G=(V, E, W), and wherein V is all vehicle connection individual compositions in G Set, E is the set that correlativity forms between vehicle connection individual, and W is the set that the degree of correlation forms between vehicle connection individual, every in E A correlativity has a degree of correlation in W to be corresponding to it;Individual v is joined for any two vehicle in car networking G1And v2, such as Fruit they have correlativity, i.e. (v1,v2) ∈ E, then their degree of correlation W (v1,v2) calculate as follows, if do not had There is correlativity, then the degree of correlation is 0:
Step 1-1, z attributive character a for joining individual relatedness computation for vehicle in car networking G is obtained1,a2,…,az, and then obtain Take corresponding with G | V | × z vehicle joins individual-attributive character matrix:
Wherein vi(aj) indicate that vehicle joins individual viIn j-th of attributive character ajOn value, 1≤i≤| V |, 1≤j≤z;
Step 1-2, individual v is joined for vehicle each in G, calculates its attributive character mean valueAnd it is counted by matrix V A Calculate covariance matrix corresponding with it:
Step 1-3, vehicle is joined into individual v1Z attributive character value be organized into one-dimensional vector and its transposition respectively,Vehicle is joined into individual v2Z attributive character value point It is not organized into one-dimensional vector and its transposition,
Step 1-4, v is calculated1And v2Between the degree of correlation:
After the relatedness computation between vehicles all in G connection individual finishes, in order to divide the global degree of correlation damage of front and back car networking Minimum is lost, G is divided into d sub- car networkings based on following steps, d takes a number in 10,15 and 20:
(1, using k-means (k- mean value) clustering algorithm G is divided into d sub- car networking IG={ G1,G2,…,Gd, and calculate The global degree of correlation of IG:
(2, following operation is repeated to sub- car networking IGIt is secondary:
(2.1, copy IG a copy IC={ G1,G2,…,Gd};
(2.2, randomly select from ICTo sub- car networking, and to each pair of sub- car networking G being selectedτAnd GεExecute following behaviour Make, obtains new sub- car networking IC '={ G1’,G2’,…,Gd' }:
(2.2.1, obtain GτIn cause and G since car networking is dividedεBetween the degree of correlation lose nτA vehicle connection individualG is obtained simultaneouslyεIn cause and G since car networking is dividedτBetween the degree of correlation lose nεA vehicle connection individual
(2.2.2, in VτMiddle selection and GεIt is highest that middle vehicle joins the individual degree of correlationA vehicle connection individual, is denoted asMeanwhile in VεMiddle selection and GτIt is highest that middle vehicle joins the individual degree of correlationA vehicle connection individual, note For
(2.2.3, by GτMiddle vehicle connection individualWith GεMiddle vehicle connection individualMutually It changes, obtains two new sub- car networking Gφ' and Gζ';
(2.3, more global degree of correlation gc (IC) and gc (IC '), if gc (IC) < gc (IC '), is updated to IC ' for IG;
(3, return to final d sub- car networking IG={ G1,G2,…,Gd};
In the step 2, in order to keep the workload of idle server balanced, take the mode of being implemented as follows by d sub- car networkings G1,G2,…,GdIt is assigned in s platform idle server: for every sub- car networking Gx, wherein 1≤x≤d obtains its vehicle connection Individual amount nx, and calculate the distribution average value upper boundThen s group is divided into this d sub- car networkings, makes score The optimal measurement of groupMinimum, wherein m θ is that the vehicle of all sub- car networkings in θ platform idle server joins individual Quantity summation;
In the step 3, vehicle connection individual v is calculated to the process flow of each Pre-Evaluation information λ interest-degree:
3 inputs, i.e. vehicle connection individual v, Pre-Evaluation information λ and traversal threshold value t are obtained first;And 4 intermediate variables are initialized, That is orderly information listIt traverses vehicle and joins individual ordered listIt traverses number b=0 and vehicle joins individual median v '=v; Then, it in the range of traversing threshold value t allows, obtains the vehicle that currently traversed vehicle connection individual v ' is directly related and joins individual collections R;And then it obtains the maximum vehicle of push ability value in R and joins individual pm, wherein pmPush ability value cap (pm) be represented by v ' with pmBetween the degree of correlation, i.e. cap (pm)=W (v ', pm);And by pmTraversal vehicle is inserted into join in individual ordered list H;Then, for pmOn the information aggregate S that is stored, obtain in S with the maximum information i of the λ interest degree of correlationm, wherein imWith the interest degree of correlation of λ inco(im, λ) and it may be expressed as:
Wherein e=2.72 is the bottom of natural logrithm,To possess i in the sub- car networking where v simultaneouslymJoin individual institute with the vehicle of λ The set of composition,And inp,λRespectively known vehicle connection individual p is to imWith the interest-degree of λ,All letters are possessed to him for p The interest-degree average value of breath;And by imIt is inserted into orderly information list I;After reaching traversal threshold value t, we have finally been based on Sequence table I and H calculate vehicle connection individual v to the interest-degree in of λv,λ:
In the step 4, when choosing specified quantity l vehicle connection individuals relevant to v in set M-R, function is usedCome Implement l, i.e.,And l vehicle connection individual before being selected in set M-R according to the degree of correlation size with v v1,v2,…,vl;Then, individual v is joined for each vehicley, wherein 1≤y≤l, based on the interest-degree calculation method in step 3, Obtain the highest information λ of interest-degree1 (y)2 (y),…,λk (y);Finally, merging (l+1) k letter that step 3 and step 4 generate Breath obtains UI={ λ12,…,λk1 (1)2 (1),…,λk (1),…,λ1 (v)2 (v),…,λk (v), interest-degree is obtained from UI Highest k information m_ λ1,m_λ2,…,m_λkAnd send vehicle connection individual v to.
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