CN104103173B - A kind of fake license plate vehicle raids supervision method - Google Patents
A kind of fake license plate vehicle raids supervision method Download PDFInfo
- Publication number
- CN104103173B CN104103173B CN201410351356.XA CN201410351356A CN104103173B CN 104103173 B CN104103173 B CN 104103173B CN 201410351356 A CN201410351356 A CN 201410351356A CN 104103173 B CN104103173 B CN 104103173B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- suspicious
- fake
- license plate
- degree
- 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.)
- Active
Links
Landscapes
- Traffic Control Systems (AREA)
- Vehicle Waterproofing, Decoration, And Sanitation Devices (AREA)
Abstract
A kind of fake license plate vehicle raids supervision method, comprises the following steps: 1) data extraction;2) journey time lower bound calculates;3) number-plate number quick hash algorithm identification vehicle is used;4) fake-licensed car suspicion degree calculates: when producing the data that Hash coding is identical and license plate number is identical, determine whether deck suspicion car, step is as follows: the time, suspicious degree calculated, collect suspicious degree to calculate, 5) most preferably deploy to ensure effective monitoring and control of illegal activities point selection: degree suspicious to vehicle fake-license is ranked up obtaining suspicious fake license plate vehicle table, when suspicious degree is more than setting threshold value, it is determined that for suspicious fake license plate vehicle;Suspicious fake license plate vehicle is carried out trajectory analysis, according to bayonet socket number and elapsed time, sets up vehicle driving trace table, using in same time slot through the most card slogan of number of times as most preferably deploying to ensure effective monitoring and control of illegal activities a little.The invention provides a kind of calculating speed, accuracy of identification is higher, reliability is good, and the fake license plate vehicle having interception function of deploying to ensure effective monitoring and control of illegal activities concurrently raids supervision method.
Description
Technical field
The invention belongs to intelligent transportation field, relate to a kind of fake license plate vehicle detection method, especially a kind of fake license plate vehicle is seized
Look into supervision method.
Background technology
Fake-licensed car is commonly called as cloning car, refers to by forging or illegally extracting in other formality such as number plate of vehicle and driving license
The vehicle that road travels.Fake-licensed car is the most of unknown origin, does not has legal procedure, the most illegally walks private car, steals and rob car, scrap car etc..
These vehicles may not claim legal number plate, travels for upper road and has to apply mechanically number plate.Meanwhile, apply mechanically other number plate,
Can random illegal running, even if being arrived by electric police grasp shoot, also will not find on oneself head, thus being at large, to society
There is the biggest harm.The low cost of deck, uses false-trademark deck, and truck can escape the expenses of taxation of the most tens thousand of unit every year, upsets warp
Ji order.Meanwhile, the legitimate rights and interests of real car owner are compromised.Therefore, fake license plate vehicle detected and implement to raid to deploy to ensure effective monitoring and control of illegal activities and be
It is highly desirable to.
In patent in published patent and examination, for the excessively idealization of the detection method design of fake license plate vehicle,
And fail to be given and effectively raid supervision method.These methods include:
Method one (patent No.: CN201110280822.6): come based on the time in bayonet socket data base and geographical position
Identify whether deck, if the theoretical shortest route time of 2 is more than the difference having recorded the elapsed time, think that this car is deck
Car.
Method two (patent No.: CN201110300956.X): bayonet socket data based on internal memory, by arranging each bayonet socket
Time threshold, when more than the elapsed time of two bayonet sockets, this time threshold then judges that this car is fake-licensed car.
Method three (patent No.: CN201310034242.8): by urban road is divided into grid, travels rail to vehicle
Mark is analyzed, if driving trace is discontinuously, is fake-licensed car.
But in actual applications, the effect of said method is unsatisfactory.It is primarily due to: (1) these methods are not
Taking into full account the front end sensors error when analyzing vehicle characteristics, such as, in actual applications, Car license recognition precision is generally
About 80%, vehicle color, vehicle etc. all probably due to external environment influence and produce certain error rate.(2) these methods
Only take into account detection fake-licensed car, do not consider how interception fake-licensed car of deploying to ensure effective monitoring and control of illegal activities.
Summary of the invention
In order to overcome the calculating speed of existing fake license plate vehicle detection method compared with slow, accuracy of identification is relatively low, reliability is poor, nothing
Deploy to ensure effective monitoring and control of illegal activities and intercept the deficiency of function, the invention provides and a kind of calculate speed, accuracy of identification is higher, reliability is good, have concurrently
The fake license plate vehicle of interception function of deploying to ensure effective monitoring and control of illegal activities raids supervision method.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of fake license plate vehicle raids supervision method, said method comprising the steps of:
1) data are extracted
Input data are: the number-plate number, vehicle, through time of bayonet socket, type of vehicle, body color, bayonet socket number, carry
Take desired data and carry out pretreatment;
2) journey time lower bound calculates
Directly according to straight line, two bayonet sockets are taken beeline, and the computing formula of journey time lower bound T is: T=H/V;Wherein,
H is two bayonet sockets short lines distances;V is maximal rate;
3) number-plate number quick hash algorithm identification vehicle is used
After a data reads in, obtain Hash coding through hash algorithm, using Hash coding as key, the number-plate number,
Body color, type of vehicle, bayonet socket number are as value;First determine whether whether the Hash table that calculated Hash coding is corresponding has
Value, not having explanation is brand-new vehicle registration, then the number-plate number, body color, type of vehicle, bayonet socket number information of vehicles are added
In Hash table;If Hash coding has existed, then inquire about whether number-plate number existence collision does not exist the identical number-plate number then
As newly-increased vehicle, the number-plate number, body color, type of vehicle, bayonet socket number information of vehicles are added Hash table;
4) fake-licensed car suspicion degree calculates
When producing the data that Hash coding is identical and license plate number is identical, it may be judged whether for deck suspicion car, step is as follows:
4.1. time suspicious degree calculates:
Calculating this car by the difference in the elapsed time of two bayonet sockets is t, as t < T, for time suspicious fake-licensed car;Suspicious degree
Computing formula be:
Wherein, p1For time confidence level, time confidence level represents the accuracy of the elapsed time data of sensor measurement;
4.2. collect suspicious degree to calculate:
Step 4.1 obtains time suspicious degree P1, step 4.1 obtains time suspicious degree P1, type of vehicle suspicious degree P2, vehicle body
Color suspicious degree P3Representative sensor measures type of vehicle, the accuracy of body color, type of vehicle suspicious degree P respectively2, vehicle body
Color suspicious degree P3Certainty of measurement according to detector obtains, and is the ratio of correct measurement vehicle fleet and total vehicle number;
The computing formula collecting suspicious degree P is as follows:
P=1-(1-P1)*(1-P2)*(1-P3)
5) most preferably deploy to ensure effective monitoring and control of illegal activities point selection
Degree suspicious to vehicle fake-license is ranked up obtaining suspicious fake license plate vehicle table, when suspicious degree is more than setting threshold value, it is determined that
For suspicious fake license plate vehicle;
Suspicious fake license plate vehicle is carried out trajectory analysis, according to bayonet socket number and elapsed time, sets up vehicle driving trace
Table, using in same time slot through the most card slogan of number of times as most preferably deploying to ensure effective monitoring and control of illegal activities a little.
Further, described step 4) in, fake-licensed car suspicion degree calculates and also includes:
4.3) the suspicious degree being combined with historical data calculates
By log history fake license plate vehicle suspicious degree Pold, and more new vehicle suspicious degree Pnew, be combined with historical data can
The formula of doubtful degree P: P=1-(1-Pold)*(1-Pnew)。
Further, described step 1) in, pretreatment includes following process:
1.1) to the number-plate number, the elapsed time has the data of disappearance to filter;
1.2) number-plate number is divided into two parts.Part I is number front two, represents area belonging to vehicle;Second
It is divided into after number five;
1.3) data being empty for type of vehicle, body color carry out completion, unified unknown for NA.
Beneficial effects of the present invention is mainly manifested in: 1, calculate speed fast: the present invention calculates correspondence by hash algorithm
The Hash coding of the number-plate number.Can be encoded by Hash quickly carry out the number-plate number lookup, remove and add up newly-increased vehicle letter
Breath.
2, suspicion degree is quantified: this method solve sensor and be probably loss of data or generation in collecting communication process
Mistake, by the quantitative analysis to suspicion degree, finds the most suspicious fake license plate vehicle.
3, self study: this algorithm, when calculating fake license plate vehicle suspicion and spending, takes full advantage of historical data, by disliking deck
Doubt the renewal of degree historical data, help vehicle supervision department to process the fake-licensed car of high suspicion degree targetedly.
4, data fusion: the elapsed time is the most only judged by this algorithm, but add type of vehicle, body color
Etc. data, this algorithm is not limited to these data, if sensor can gather more specifically data, it is also possible to very easily
Carry out suspicion degree calculating.
5, most preferably deploy to ensure effective monitoring and control of illegal activities a little: this algorithm, by the vehicle higher to deck suspicion degree, carries out trajectory analysis.Find specific
The bayonet socket number that this car of period frequently occurs, deploys to ensure effective monitoring and control of illegal activities a little using this point as optimal inspection, has preferable realistic meaning.
Accompanying drawing explanation
Fig. 1 is that fake license plate vehicle raids fake-licensed car overhaul flow chart in supervision method.
Fig. 2 is the flow chart of deck suspicion degree.
Fig. 3 is the flow chart most preferably deployed to ensure effective monitoring and control of illegal activities a little.
Fig. 4 is the schematic diagram deployed to ensure effective monitoring and control of illegal activities.
Detailed description of the invention
The invention will be further described below in conjunction with the accompanying drawings.
With reference to Fig. 1~Fig. 4, a kind of fake license plate vehicle raids supervision method, said method comprising the steps of:
1) data are extracted
Extract desired data and carry out simple pretreatment;There is the highest versatility in the source of data, form, input data
For: the number-plate number, vehicle are through data such as time of bayonet socket, type of vehicle, body color, bayonet socket numbers.Pretreatment include as
Under:
To the number-plate number, the elapsed time has the data of disappearance to filter.
According to country's common number-plate number specification, the number-plate number is divided into two parts.Part I is number front two, generation
Area belonging to table vehicle;Part II is after number five, is made up of two English alphabets and three numerals.Data are extracted and are also pressed
This regular partition number-plate number.
The data being empty for type of vehicle, body color carry out completion, unified unknown for NA.
2) journey time lower bound calculates
The shortest path between each bayonet socket can be obtained, for convenience of calculation, by two bayonet sockets by bayonet socket information data table
Directly take beeline according to straight line.Present invention assumes that vehicle maximal rate is 100,000 ms/h, the meter of journey time lower bound T
Calculation formula is: T=H/V;Wherein, H is two bayonet sockets short lines distances;V is maximal rate.
3) number-plate number quick hash algorithm identification vehicle is used
After a data reads in, through hash algorithm, using Hash code as key, the number-plate number, body color, vehicle class
Type, bayonet socket number etc. are as value.First determine whether whether the Hash table that calculated Hash coding is corresponding has value, do not have the explanation to be
Brand-new vehicle registration, then add information of vehicles such as the number-plate number, body color, type of vehicle, bayonet socket numbers in Hash table;As
Really Hash coding has existed, then inquire about whether number-plate number existence collision does not exist the identical number-plate number then as newly-increased car
, the information of vehicles such as the number-plate number, body color, type of vehicle, bayonet socket number are added Hash table;
The quick hash algorithm of the number-plate number: Hash is exactly that random length entered through hash algorithm, is transformed into fixing
The output of length, this output is exactly hash value.This conversion is a kind of mapping, it is, input value must have unique Hash
Value is corresponding therewith.Different input values may generate identical Hash code, and uniquely can not determine defeated from Hash code
Enter value.Can quickly search data by searching Hash coding, big data query is had higher speed.Mathematical description is as follows:
Input value: key1;key2;
Hash code: F (key1);F(key2);
Wherein, F is Hash function.
As key1 ≠ key2, but F (key1)=F (key2);This phenomenon is referred to as collision.Compare herein by secondary and disappear
Except collision.
Hash function F method for expressing is as follows:
Wherein, carnumber be through data extract the number-plate number after 5;Carnumber.charAt (j) method will
Character corresponding to jth position is converted into corresponding A SCII code.Hash code is drawn through interative computation, shift operation, bit arithmetic.
4) fake-licensed car suspicion degree calculates
Through previous step, when produce Hash code-phase with and the identical data of license plate number, it may be judged whether for deck suspicion car,
Specifically comprise the following steps that
4.1. time suspicious degree calculates:
Calculating this car by the difference in the elapsed time of two bayonet sockets is t, and this bayonet socket theory minimum time passes through step 2)
Step can be T, as t < T, for time suspicious fake-licensed car.
The computing formula of suspicious degree is: P1=[1-(t/T) ^2] * p1
Wherein, p1For time confidence level, time confidence level represents the accuracy of the elapsed time data of sensor measurement.
4.2. collect suspicious degree to calculate:
One partial data is included the number-plate number, body color, type of vehicle, bayonet socket number, elapsed time etc.;Carry out
Above-mentioned steps can obtain time suspicious degree P1,
Type of vehicle suspicious degree P2, body color suspicious degree P3Respectively representative sensor measures type of vehicle, body color
Accuracy, type of vehicle suspicious degree P2, body color suspicious degree P3Certainty of measurement according to detector obtains, and is correct measurement car
The ratio of sum and total vehicle number;
The computing formula collecting suspicious degree P is as follows:
P=1-(1-P1)*(1-P2)*(1-P3)
4.3. the suspicious degree being combined with historical data calculates
The present invention passes through log history fake license plate vehicle suspicious degree Pold, and update Current vehicle suspicious degree Pnew, when certain can
Doubtful fake license plate vehicle occurs suspicious record repeatedly, by can improve the suspicious degree of this car with the combination of historical data, finally to can
The vehicle that doubtful degree is higher carries out trajectory analysis and is most preferably deployed to ensure effective monitoring and control of illegal activities a little.
The formula of the suspicious degree P being combined with historical data: P=1-(1-Pold)*(1-Pnew)
6) most preferably deploy to ensure effective monitoring and control of illegal activities point selection
Degree suspicious to vehicle fake-license is ranked up obtaining suspicious fake license plate vehicle table, when suspicious degree is more than setting threshold value, it is determined that
For suspicious fake license plate vehicle;
Fake license plate vehicle is carried out trajectory analysis, according to bayonet socket number and elapsed time, vehicle driving trace can be set up
Table.Using in same time slot through the most card slogan of number of times as most preferably deploying to ensure effective monitoring and control of illegal activities a little.
The present embodiment uses the data of 821 the bayonet socket sensor acquisition in Hangzhou.Bayonet socket positional information form is as follows:
Bayonet socket number | Longitude | Latitude |
248092 | 120.1517 | 30.2686 |
2148197 | 120.1242 | 30.369 |
··· | ··· | ··· |
Extract the data of the one week all bayonet socket record in Hangzhou, 47483502 altogether.Form is as follows:
The number-plate number | Elapsed time | Type of vehicle | Body color | Bayonet socket number |
Zhejiang A0X073 | 2013/6/70:1:23 | 1 | 1 | 2147704 |
Zhejiang A3X172 | 2013/6/70:7:46 | 1 | -1 | 2633 |
··· | ··· | ··· | ··· | ··· |
Journey time lower bound computing module, it is as follows that GPS calculates beeline method between bayonet socket:
EARTH_RADIUS=6378137;
Longitude and latitude turns radian formula: rad=d* π/180.0;
Ask beeline method as follows by the longitude and latitude of two bayonet sockets:
RadLat1=rad (y1);
RadLat2=rad (y2);
A=radLat1-radLat2;
B=rad (x1)-rad (x2);
S=2*Math.asin (Math.sqrt (Math.pow (Math.sin (a/2), 2)+Math.cos (radLat1) *
Math.cos(radLat2)*Math.pow(Math.sin(b/2),2)));
S=s*EARTH_RADIUS;
Wherein A bayonet socket longitude and latitude is x1, y1;B bayonet socket longitude and latitude is x2, y2.S is the beeline between AB bayonet socket.
Hash yardage is calculated: according to the quick hash algorithm of the above-mentioned number-plate number through five iteration, can obtain corresponding Hash
Code is as follows:
The number-plate number | Iteration for the first time | Iteration for the second time | Iteration for the third time | 4th iteration | Hash code |
Zhejiang A0X073 | 48 | 856 | 13744 | 219959 | 3519395 |
Zhejiang A3X172 | 51 | 904 | 14513 | 232263 | 3716258 |
··· | ··· | ··· | ··· | ··· | ··· |
Fake-licensed car suspicion degree calculates: certain car is through 8 minutes two bayonet socket used times of A, B;This bayonet socket shortest route time is 10
Minute;This car type of vehicle, body color all change.This car suspicious degree of history deck is 0.5;The then deck suspicion of this car
Degree is calculated as follows:
Time, suspicious degree calculated:
Computing formula according to suspicious degree: P1=[1-(t/T) ^2] * p1
Can obtain: Wherein p1=0.5
Collect suspicious degree to calculate:
According to collecting suspicious degree computing formula: P=1-(1-P1)*(1-P2)*(1-P3)
According to real data with expertise through analyzing, make sensor to type of vehicle, the recognition accuracy of body color
It is respectively P2=P3=0.3
Can obtain: P=1-(1-P1)*(1-P2)*(1-P3)=0.5982
The suspicious degree being combined with historical data calculates
Formula according to being combined with historical data: P=1-(1-Pold)*(1-Pnew)
Can obtain: P=1-(1-Pild)*(1-Pnew)=0.7991
Combining by collecting suspicious degree calculating and degree suspicious with history, this suspicious degree of car deck more newly obtained is
0.7991, when this car occurs deck doubtful situations repeatedly, this suspicious degree also may proceed to raise.The vehicle higher to suspicious degree, hands over
Logical administration section can investigate targetedly, finds fake license plate vehicle in time.
Point selection of most preferably deploying to ensure effective monitoring and control of illegal activities method: analyze by one week 47483502 data is carried out the suspicious degree of deck, have found
The information of vehicles that suspicious degree is the highest is as follows:
The number-plate number | Elapsed time | Type of vehicle | Body color | Bayonet socket number |
Zhejiang A3B108 | 2013/6/76:18:49 | 2 | 1 | 2148652 |
Zhejiang A3B108 | 2013/6/76:15:38 | 2 | -1 | 2652 |
··· | ··· | ··· | ··· | ··· |
Dividing time into a hours groove, each opens the number of pass times being stuck in this time slot to add up this vehicle:
By bayonet socket number-time slot information table find this car between 5 o'clock to 6 o'clock have 4 times through 2148801 bayonet sockets, as
Under:
The number-plate number | Elapsed time | Type of vehicle | Body color | Bayonet socket number |
Zhejiang A3B108 | 2013/6/85:51:23 | 2 | 1 | 2148801 |
Zhejiang A3B108 | 2013/6/115:25:47 | 2 | 1 | 2148801 |
Zhejiang A3B108 | 2013/6/125:51:14 | 2 | 1 | 2148801 |
Zhejiang A3B108 | 2013/6/135:43:53 | 2 | 1 | 2148801 |
Can select to check to confirm that this car is to this car at 2148801 bayonet sockets between 5: 20 to 6 by this table
No for fake license plate vehicle.
Zhejiang A3B108 car driving trace such as Fig. 4, labelling point (stain) is 2148801 bayonet socket geographical position.
Claims (3)
1. a fake license plate vehicle raids supervision method, it is characterised in that: said method comprising the steps of:
1) data are extracted
Input data are: the number-plate number, vehicle, through time, type of vehicle, body color and the bayonet socket number of bayonet socket, extract institute
Need data and carry out pretreatment;
2) journey time lower bound calculates
Directly according to straight line, two bayonet sockets are taken beeline, and the computing formula of journey time lower bound T is: T=H/V;Wherein, H is
Two bayonet sockets short lines distance;V is maximal rate;
3) number-plate number quick hash algorithm identification vehicle is used
After a data reads in, obtain Hash coding through hash algorithm, Hash is encoded as key, the number-plate number, vehicle body
Color, type of vehicle and bayonet socket number are as value;First determine whether whether the Hash table that calculated Hash coding is corresponding has value,
Not having explanation is brand-new vehicle registration, then the number-plate number, body color, type of vehicle and bayonet socket number information of vehicles are added
In Hash table;If Hash coding has existed, then inquire about whether number-plate number existence collision does not exist the identical number-plate number then
As newly-increased vehicle, the number-plate number, body color, type of vehicle and bayonet socket number information of vehicles are added Hash table;
4) fake-licensed car suspicion degree calculates
When producing the data that Hash coding is identical and license plate number is identical, it may be judged whether for deck suspicion car, step is as follows:
4.1. time suspicious degree calculates:
Calculating vehicle is t by the difference in the elapsed time of two bayonet sockets, as t < T, for time suspicious fake-licensed car;
The computing formula of suspicious degree is:
Wherein, p1For time confidence level, time confidence level represents the accuracy of the elapsed time data of sensor measurement;
4.2. collect suspicious degree to calculate:
Step 4.1 obtains time suspicious degree P1, type of vehicle suspicious degree P2, body color suspicious degree P3Representative sensor is surveyed respectively
Amount type of vehicle, the accuracy of body color, type of vehicle suspicious degree P2, body color suspicious degree P3Measurement according to detector
Precision obtains, and is the ratio of correct measurement vehicle fleet and total vehicle number;
The computing formula collecting suspicious degree P is as follows:
P=1-(1-P1)*(1-P2)*(1-P3)
5) most preferably deploy to ensure effective monitoring and control of illegal activities point selection
Degree suspicious to vehicle fake-license is ranked up obtaining suspicious fake license plate vehicle table, when suspicious degree is more than setting threshold value, it is determined that for can
Doubt fake license plate vehicle;
Suspicious fake license plate vehicle is carried out trajectory analysis, according to bayonet socket number and elapsed time, sets up vehicle driving trace table, will
The interior card slogan most through number of times of same time slot is as most preferably deploying to ensure effective monitoring and control of illegal activities a little.
2. a kind of fake license plate vehicle as claimed in claim 1 raids supervision method, it is characterised in that: described step 4) in, deck
Car suspicion degree calculates and also includes:
4.3) the suspicious degree being combined with historical data calculates
By log history fake license plate vehicle suspicious degree Pold, and more new vehicle suspicious degree Pnew, the suspicious degree P that is combined with historical data
Formula: P=1-(1-Pold)*(1-Pnew)。
3. a kind of fake license plate vehicle as claimed in claim 1 or 2 raids supervision method, it is characterised in that: described step 1) in, in advance
Process includes following process:
1.1) to the number-plate number, the elapsed time has the data of disappearance to filter;
1.2) number-plate number being divided into two parts, Part I is number front two, represents area belonging to vehicle;Part II is
After number five;
1.3) data being empty for type of vehicle, body color carry out completion, unified unknown for NA.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410351356.XA CN104103173B (en) | 2014-07-22 | 2014-07-22 | A kind of fake license plate vehicle raids supervision method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410351356.XA CN104103173B (en) | 2014-07-22 | 2014-07-22 | A kind of fake license plate vehicle raids supervision method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104103173A CN104103173A (en) | 2014-10-15 |
CN104103173B true CN104103173B (en) | 2016-08-24 |
Family
ID=51671284
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410351356.XA Active CN104103173B (en) | 2014-07-22 | 2014-07-22 | A kind of fake license plate vehicle raids supervision method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104103173B (en) |
Families Citing this family (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105336164B (en) * | 2015-10-27 | 2017-09-26 | 杭州电子科技大学 | The wrong bayonet socket positional information automatic identifying method analyzed based on big data |
CN105513368B (en) * | 2015-11-26 | 2017-10-17 | 银江股份有限公司 | A kind of false-trademark car screening technique based on uncertain information |
CN105913668B (en) * | 2016-07-04 | 2018-05-11 | 中国电子科技集团公司第二十八研究所 | Method is surveyed in a kind of orientation deck car test based on huge traffic data statistics |
CN107038865A (en) * | 2017-04-06 | 2017-08-11 | 北京易华录信息技术股份有限公司 | A kind of clone's vehicle position determination method and system based on vehicle electron identifying |
CN107067736B (en) * | 2017-04-12 | 2019-10-08 | 安徽超远信息技术有限公司 | Fake-licensed car analysis method and its system based on time road network |
CN107798879A (en) * | 2017-10-25 | 2018-03-13 | 济南浪潮高新科技投资发展有限公司 | A kind of method of Intelligent Recognition fake-licensed car |
CN110019344A (en) * | 2017-12-27 | 2019-07-16 | 深圳市优必选科技有限公司 | Suspicious vehicle control method, system and terminal equipment |
CN108389385B (en) * | 2018-04-03 | 2020-09-18 | 北京万集科技股份有限公司 | Method and device for intercepting overrun vehicle, electronic equipment and storage medium |
CN110716929B (en) * | 2018-07-13 | 2023-01-24 | 杭州海康威视***技术有限公司 | Control and deployment processing method, device and equipment |
CN111179603B (en) * | 2018-11-09 | 2021-03-02 | 杭州海康威视数字技术股份有限公司 | Vehicle identification method and device, electronic equipment and storage medium |
CN109741605A (en) * | 2018-12-25 | 2019-05-10 | 深圳市天彦通信股份有限公司 | Vehicle monitoring method and relevant apparatus |
CN109859492A (en) * | 2019-03-27 | 2019-06-07 | 成都市公安科学技术研究所 | A kind of system and method identifying fake-licensed car |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101261771A (en) * | 2008-03-14 | 2008-09-10 | 康华武 | An automatic checking method for vehicle identity on the road |
CN101540105A (en) * | 2009-04-15 | 2009-09-23 | 四川川大智胜软件股份有限公司 | Fake-licensed car detection method based on number-plate identification and gridding supervision |
CN101587643A (en) * | 2009-06-08 | 2009-11-25 | 宁波大学 | Identification method of fake-licensed cars |
JP2014002534A (en) * | 2012-06-18 | 2014-01-09 | Toshiba Corp | Vehicle type determination device and vehicle type determination method |
CN103730007A (en) * | 2013-12-20 | 2014-04-16 | 南威软件股份有限公司 | Checkpoint passing car real-time comparison and analysis alarming method and alarming system thereof |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
SG10201504774XA (en) * | 2005-06-10 | 2015-07-30 | Accenture Global Services Ltd | Electronic vehicle identification |
-
2014
- 2014-07-22 CN CN201410351356.XA patent/CN104103173B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101261771A (en) * | 2008-03-14 | 2008-09-10 | 康华武 | An automatic checking method for vehicle identity on the road |
CN101540105A (en) * | 2009-04-15 | 2009-09-23 | 四川川大智胜软件股份有限公司 | Fake-licensed car detection method based on number-plate identification and gridding supervision |
CN101587643A (en) * | 2009-06-08 | 2009-11-25 | 宁波大学 | Identification method of fake-licensed cars |
JP2014002534A (en) * | 2012-06-18 | 2014-01-09 | Toshiba Corp | Vehicle type determination device and vehicle type determination method |
CN103730007A (en) * | 2013-12-20 | 2014-04-16 | 南威软件股份有限公司 | Checkpoint passing car real-time comparison and analysis alarming method and alarming system thereof |
Also Published As
Publication number | Publication date |
---|---|
CN104103173A (en) | 2014-10-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104103173B (en) | A kind of fake license plate vehicle raids supervision method | |
CN101565925B (en) | Pavement distress investigating and treating method | |
CN105444730A (en) | Time-space characteristic and cross-border mining identification method for multi-source data monitoring mining area deformation | |
CN102200008B (en) | Reservoir effectiveness identification method based on electrical imaging logging | |
CN103105615B (en) | False detection method of satellite navigation signals and satellite navigation positioning receiver | |
CN106610981A (en) | Verification and upgrading method and system for road information in electronic map | |
CN104567906A (en) | Beidou-based urban road network vehicle path planning method and device | |
CN102722907B (en) | Geometric modeling method based on pipe factory point cloud | |
CN104504099A (en) | Position-trajectory-based travel state splitting method | |
CN104111073A (en) | Method and device for identifying inaccurate paths in map data | |
DK1811480T3 (en) | Automatic road payment system based solely on satellite navigation, taking into account position accuracy and approach | |
Zheng et al. | Evaluating the accuracy of GPS-based taxi trajectory records | |
CN106918341A (en) | Method and apparatus for building map | |
Sultan et al. | Extracting spatial patterns in bicycle routes from crowdsourced data | |
CN105355042A (en) | Road network extraction method based on taxi GPS | |
CN103093625B (en) | City road traffic condition real-time estimation method based on reliability verification | |
CN106297304A (en) | A kind of based on MapReduce towards the fake-licensed car recognition methods of extensive bayonet socket data | |
CN105740505A (en) | GPS-RTK technology based road space line shape recovery method | |
CN104900057A (en) | City expressway main and auxiliary road floating vehicle map matching method | |
CN104408920A (en) | Checkpoint traffic information-based method for judging traffic violation of long-distance passenger vehicles | |
Li et al. | Toward a crowdsourcing solution to identify high-risk highway segments through mining driving jerks | |
CN106226785A (en) | Anomalous of the ionosphere monitoring model method for building up and device | |
Jariyasunant et al. | Overcoming battery life problems of smartphones when creating automated travel diaries | |
CN106980029B (en) | Vehicle overspeed judgment method and system | |
Yokota et al. | Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CP01 | Change in the name or title of a patent holder |
Address after: 310012 floor 1, building 1, No. 223, Yile Road, Xihu District, Hangzhou City, Zhejiang Province Patentee after: Yinjiang Technology Co.,Ltd. Address before: 310012 floor 1, building 1, No. 223, Yile Road, Xihu District, Hangzhou City, Zhejiang Province Patentee before: ENJOYOR Co.,Ltd. |
|
CP01 | Change in the name or title of a patent holder |