CN109190924A - Video number plate Data Quality Analysis method - Google Patents
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- CN109190924A CN109190924A CN201810914240.0A CN201810914240A CN109190924A CN 109190924 A CN109190924 A CN 109190924A CN 201810914240 A CN201810914240 A CN 201810914240A CN 109190924 A CN109190924 A CN 109190924A
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Abstract
The present invention provides a kind of video number plate Data Quality Analysis method, the integrality of device data acquisition is analyzed based on video number plate identification equipment on-line situation, it is based further on video number plate historical data and vehicle checker device data quality and gps data information analyzes data reasonability, automatic early-warning is carried out to equipment fault；This kind of video number plate Data Quality Analysis method, " data integrity-flow reasonability-time reasonability " the data quality checking system of building, data based on number plate identification equipment on-line situation, data on flows analysis and the identification equipment acquisition of time difference analysis conditions trick-plate carry out analysis detection, unit exception situation and automatic early-warning are determined according to abnormal data, so that manager is effectively grasped monitoring device health status, management is repaired to warping apparatus in time.
Description
Technical field
The present invention relates to a kind of video number plate Data Quality Analysis methods.
Background technique
" number plate identification " carries out license number automatic identification using vehicle dynamic video, is electronic police, smart card
The principle that the equipment such as mouth mainly acquire.Number plate identification equipment is widely used in urban transportation control at present, but by equipment work
Make the influence of the uncertain factors such as state, network transmission, road traffic condition, ambient enviroment, number plate identifies equipment, and there may be adopt
Collect data quality problem, such as loss of data, Car license recognition are abnormal, time data wander, and data quality problem is by direct shadow
Electronic police, intelligent bayonet, ETC, self-service parking detection system reliability and stability are rung, is caused not for traffic control law enforcement etc.
Benefit influences.Therefore for the traffic data quality of number plate identification equipment acquisition, it should well-equipped data quality checking method,
It is effectively isolated out abnormal data, early warning is carried out to equipment, administrative staff is made to find maintenance process in time.
The research of number plate data accuracy, such as patent are focused primarily upon for number plate identification device data matter quantifier elimination at present
CN 201510559740.3 proposes a kind of method, apparatus and system for improving Car license recognition accuracy, patent CN
201410638356.8 propose that a kind of antidote of video metadata under traffic scene, patent CN201610270472.8 propose
A kind of Car license recognition intelligence mistake method and system based on license plate rule and space-time accessibility, patent CN
201610160748.7 proposition bayonets cross the detection Control for Dependability method and system of vehicle, and patent CN 201710115608.2 is mentioned
The tollgate devices quality of data method of inspection based on location data out.Wherein patent CN 201410638356.8 and CN
201610270472.8 being all based on number plate data analysis track of vehicle to eliminate anomalous identification information, patent CN
201610160748.7 obtain the data quality accessments indexs such as data volume, discrimination by the statistical analysis of history detection data
Threshold value, and then differentiate the quality of data risk of tollgate devices, rejecting abnormalities equipment, patent CN 201610160748.7 introduces more
Source data consistency concept analyzes the quality of data based on GPS positioning data.
In conclusion though existing research carries out identification study on accuracy and quality of data basis for number plate identification equipment
Research, but with the development of the technologies such as wisdom traffic and artificial intelligence machine study, it can be in the prior art and data research
On the basis of, it compares with time, space, transverse direction, longitudinal various dimensions trick-plate data, while other multi-source datas being assisted
Analysis, to realize the Data Quality Analysis detection of number plate data, effective early warning warping apparatus realizes the synchronous detection of device data
Management, provides quality data for traffic control.
Summary of the invention
The object of the present invention is to provide a kind of video number plate Data Quality Analysis method, solution is existing in the prior art such as
The problem of what effective early warning warping apparatus realizes the synchronous detection management of device data, provides quality data for traffic control.
The present invention proposes a kind of video number plate Data Quality Analysis method, by identifying number to equipment on-line state trick-plate
According to integrity analysis led to by acquiring data on flows comparison to flow rationality checking with historical traffic and vehicle checker equipment
It crosses and the GPS data in road network with mobile unit vehicle is analyzed, calculate average time difference to data time rationality checking, from
And effectively judge warping apparatus and automatic early-warning, identify that equipment management provides support foundation for traffic control department number plate, while can
Reliable accurate data is provided for traffic planning and management.
The technical solution of the invention is as follows:
A kind of video number plate Data Quality Analysis method adopts device data based on video number plate identification equipment on-line situation
The integrality of collection is analyzed, and video number plate historical data and vehicle checker device data quality and GPS data are based further on
Information analyzes data reasonability, carries out automatic early-warning to equipment fault, includes the following steps,
S1, identify that equipment on-line situation analyzes the integrality of devices collect data according to number plate；
S2, identify that the flow information of devices collect data carries out analytical control to the reasonability of the quality of data according to number plate；
S3, the temporal information of GPS data and number plate identification devices collect data with positioning device vehicle is carried out pair
Than realizing that number plate identifies data time analysis on its rationality；
S4, number plate identification unit exception and automatic early-warning.
Further, step S1 specifically,
The online rate analysis of S11, time dimension, it is each to equipment based on number plate identification equipment on-line state in statistical time section
Online situation is for statistical analysis in unit interval, if number of segment ratio is lower than online rate threshold value when online, goes to step S4,
Otherwise step S12 is gone to；
The online rate analysis of S12, Spatial Dimension, extracts all number plates in road network system and identifies equipment on-line data, to each list
Always online rate is calculated equipment in the period of position, if always online rate is lower than the online threshold value of system equipment to equipment, then it is assumed that number plate
There is the system failure and automatic early-warning in identifying system, otherwise goes to S2 step.
Further, step S2 specifically,
S21, data traffic vertical analysis is realized by comparing analysis with device history data, unit interval number plate is known
The data of other equipment acquisition are compared and analyzed with same period historical data, if vehicle flowrate exceeds unit in unit interval
Between historical traffic threshold interval in section, then devices collect data in the unit interval is determined as suspicious abnormal data, and turn
To step S22, otherwise it is assumed that data are normal in the unit interval, if all unit time segment datas are just in statistical time section
Often go to step S3；
S22, by with number plate identify implantation of device point at vehicle checker device data mass ratio to realize multi-source data stream
Amount laterally compares analysis.
Further, step S22 specifically,
S221, it extracts in the suspicious abnormal data affiliated period with the data of point vehicle checker equipment acquisition, analyzes car test
Device devices collect data quality, if the data that all devices acquire in the point abnormal time section are suspicious abnormal data,
Think to go to step S3 there may be time anomaly, otherwise go to next step.
S222, collect all suspicious abnormal datas in statistical time section, if suspicious abnormal data amount accounts for the ratio of total data
Greater than suspicious data proportion threshold value, step S4 is gone to, otherwise goes to step S3.
Further, step S3 specifically,
S31, the GPS data in road network with vehicle positioning equipment vehicle is extracted, location data is matched in road network, into
One step establishes equipment section contingency table according to number plate identification device location and road network road section information in road network, extracts each number plate
Identify the GPS data within the scope of equipment GPS data；
S32, by within the scope of equipment GPS data and number plate identification data compare and analyze, according to number plate identify equipment
Collected number plate of vehicle information extraction goes out GPS data of the number plate in data area, obtains the mean time between two numerical value
Between it is poor, if in unit interval average time difference be greater than threshold value, go to step S4, otherwise it is assumed that number plate identification equipment acquire number
According to normal.
Further, average in the unit interval of the GPS data in step S32 within the scope of equipment and number plate identification data
Time difference, specifically,
In formula, tGPSFor the data time of GPS gathers, tALPRThe time value of data is identified for number plate, n is that number plate identification is set
The data volume of GPS data is collected in standby range.
Further, in step S32, the GPS data based on more vehicles of approach in the unit time is to unit interval
Interior average time differenceIt is calculated, it may be assumed thatN is vehicle number in formula.
The beneficial effects of the present invention are:
One, this kind of video number plate Data Quality Analysis method, " data integrity-flow reasonability-time is reasonable for building
Property " data quality checking system, based on number plate identification equipment on-line situation, data on flows analysis and time difference analysis conditions pair
Number plate identifies that the data of equipment acquisition carry out analysis detection, determines unit exception situation and automatic early-warning according to abnormal data, makes
Manager effectively grasps monitoring device health status, repairs management to warping apparatus in time.
Two, the proposition multi-source data analysis innovated of the present invention identifies by number plate based on laying point, to the point other
The quality of data Synchronization Analysis of equipment determines whether equipment is abnormal, improves and sets based on all devices quality of data unusual condition
The accuracy of standby abnormal determination and number plate data acquisition traffic flow data.
Three, the present invention proposes to be provided with the taxi of positioning device, the GPS number of bus commerial vehicle based on equipment range
It compares according to number plate data, is analyzed based on average time difference trick-plate data time reasonability.
Detailed description of the invention
Fig. 1 is the flow diagram of video number plate Data Quality Analysis method of the embodiment of the present invention.
Fig. 2 is that the GPS data range of number plate identification equipment 4 in embodiment illustrates schematic diagram.
Wherein, 1- number plate identifies equipment 4；2-GPS data area.
Specific embodiment
The preferred embodiment that the invention will now be described in detail with reference to the accompanying drawings.
Embodiment
A kind of video number plate Data Quality Analysis method of embodiment, using single number plate identification equipment as object, by right
Data integrity, data traffic reasonability and data time reasonability tripartite's surface analysis identify that abnormal data, trick-plate are known
Other equipment fault carries out early warning, judges that number plate identifies the quality of data in time, early warning faulty equipment provides high quality for traffic control
Data, safeguards system stable operation.
A kind of video number plate Data Quality Analysis method adopts device data based on video number plate identification equipment on-line situation
The integrality of collection is analyzed, and video number plate historical data and vehicle checker device data quality and GPS data are based further on
Information analyzes data reasonability, carries out automatic early-warning to equipment fault.It is specific as follows such as Fig. 1:
S1. identify that equipment on-line situation analyzes the integrality of devices collect data according to number plate.
S11. the online rate analysis of time dimension.It is each to equipment based on number plate identification equipment on-line state in statistical time section
Online situation is for statistical analysis in unit interval, if number of segment ratio is lower than online rate threshold value when online, goes to step S4,
Otherwise step S12 is gone to.Wherein statistical time section is generally as unit of hour or day, unit interval 5min, 10min or
15min。
S12. the online rate analysis of Spatial Dimension.It extracts all number plates in road network system and identifies equipment on-line data, to each list
Always online rate is calculated equipment in the period of position, if always online rate is lower than the online threshold value of system equipment to equipment, then it is assumed that number plate
There is the system failure and automatic early-warning in identifying system, otherwise goes to S2 step.
Under normal circumstances, road network system refers to the number plate identification equipment of all similar layings in compass of competency, while can root
According to number plate, identification equipment manufacturer carries out calculating analysis to the online situation of each plant equipment respectively, carries out to the producer of unit exception pre-
It is alert.
S2. identify that the flow information of devices collect data carries out analytical control to the reasonability of the quality of data according to number plate.
S21. data traffic vertical analysis is realized by comparing analysis with device history data.
The data of unit interval number plate identification equipment acquisition are compared and analyzed with same period historical data, if single
Equipment in the unit interval is then acquired number beyond historical traffic threshold interval in unit interval by vehicle flowrate in the period of position
According to being determined as suspicious abnormal data, and step S22 is gone to, otherwise it is assumed that data are normal in the unit interval, if statistical time
All unit time segment datas normally go to step S3 in section.Wherein unit time historical traffic threshold value is according to historical data
15% median of average maximum and minimum value or data and 85% median determine.
Under normal circumstances, to improve historical data accuracy using week and time point historical data as unit of account, wherein
The mean values section of each all 7:00-7:15 periods early in the morning in the passing time is corresponded in period Monday 7:00-7:15.
S22. by identifying that vehicle checker device data mass ratio is to realization multi-source data stream at implantation of device point with number plate
Amount laterally compares analysis.
S221. it extracts in the suspicious abnormal data affiliated period with the data of point vehicle checker equipment acquisition, analyzes other
Devices collect data quality is recognized if the data that all devices acquire in the point abnormal time section are suspicious abnormal data
For there may be time anomalies, step S3 is gone to, next step is otherwise gone to.
Under normal circumstances, a crossing or section are not only laid with number plate identification acquisition equipment while being also possible to be laid with vehicle
Examine device equipment, wherein consistent with step S21 for vehicle checker data quality checking step, according to historical traffic flows numerical value pair
It is whether more abnormal than determining data.
S222. collect all suspicious abnormal datas in statistical time section, if suspicious abnormal data amount accounts for the ratio of total data
Greater than suspicious data proportion threshold value, step S4 is gone to, otherwise goes to step S3.
S3. the temporal information of GPS data and number plate identification devices collect data with positioning device vehicle is carried out pair
Than realizing that number plate identifies data time analysis on its rationality.
S31. the GPS data in road network with vehicle positioning equipment vehicle is extracted, location data is matched in road network, into
One step establishes equipment section contingency table according to number plate identification device location and road network road section information in road network, extracts each number plate
Identify the GPS data within the scope of equipment GPS data.
Under normal circumstances, the commerial vehicles such as vehicle selection taxi, bus.On the other hand, can based on GPS data when
Between sequentially determine vehicle substantially traffic direction, to may be passed through determining data area of letting pass by vehicle.Such as one ten
Word crossing, the number plate of eastern entrance identify equipment, and GPS data only may alternatively appear in south, west, three, north after the appearance of eastern entrance
At direction, to exclude the GPS data of eastern import opposite direction, while it will appear in from 20-50 within eastern entrance driveway stop line
It is set as data area at rice, to extract effective GPS data.
S32. the GPS data within the scope of equipment is compared and analyzed with number plate identification data, equipment is identified according to number plate
Collected number plate of vehicle information extraction goes out GPS data of the number plate in data area, obtains the mean time between two numerical value
Between it is poorThat is:
In formula, tGPSFor the data time of GPS gathers, tALPRThe time value of data is identified for number plate, n is that number plate identification is set
The data volume of GPS data is collected in standby range.
Further, the GPS data based on more vehicles of approach in the unit time is to the average time in unit interval
DifferenceIt is calculated, it may be assumed that
In formula, N is vehicle number.
If average time difference is greater than threshold value in unit interval, step S4 is gone to, otherwise it is assumed that number plate identification equipment is adopted
It is normal to collect data.
S4. number plate identification unit exception and automatic early-warning.
Embodiment method simultaneously can be measured in real time data, and according to the same day, real-time data volume was complete to data so far
Property, data on flows reasonability and time reasonability are tested and analyzed.
One specific example of embodiment is as follows:
The detection data of one day is chosen, unit interval chooses 15min.
Device data integrality is detected according to step S1:
To equipment on-line situation summarizes in 2595 sets of equipment unit time sections in road network, when to this day equipment on-line
Between section threshold value calculated, choose wherein 10 sets equipment numerical value progress example:
By comparing with online rate threshold value (selected threshold 80%), discovering device 3 is abnormal to carry out unit exception early warning.Into
Always online situation is for statistical analysis to road network equipment in unit time section for one step, because being all larger than total online rate threshold value 50%,
Therefore system exception is not present.
It is detected according to the data traffic reasonability that step S2 trick-plate equipment acquires:
By taking equipment 4 as an example, data traffic is 311 when detecting 7:00-7:15 in morning day, is chosen according to historical traffic average
15% median and 85% median are flow threshold section, as [280,320], because being then regime values in it.Similarly
Detection day each period data traffic is compared, abnormal time section is extracted, specific as follows:
| Period | Flow number | Historical traffic threshold interval |
| 2:00-2:15 | 73 | [50,70] |
| 2:15-2:30 | 65 | [25,40] |
For equipment 4 in first crossing south entrance driveway, which not only has device number board to identify equipment (electronic police) 4, goes back cloth
Equipped with microwave vehicle checker, extract the vehicle flowrate of microwave vehicle checker 2:00-2:15 and 2:15-2:30, by with history wagon flow
Data are measured it was found that the period is abnormal, it is normal to be defaulted as equipment 4.
Carrying out detection discovery to remaining equipment simultaneously is normal device.
It is detected according to the time reasonability that step S3 trick-plate equipment acquires:
By taking equipment 4 as an example, (first crossing is a crossroad to GPS data range, and number plate identifies equipment 4 as shown in Figure 2
In southern entrance driveway).In Fig. 2, label 1 is that number plate identifies equipment 4；Label 2 is GPS data range.
GPS data from taxi is extracted, the GPS number in data area is extracted according to data time series and location information
According to comparing calculating by the number plate data time information that taxi number plate is captured with equipment 4 respectively, obtain average time
Difference, (only enumerating partial time period) specific as follows:
| Period | Average time difference (s) |
| 7:00-7:15 | 38 |
| 7:15-7:30 | 47 |
| 7:30-7:45 | 62 |
| 7:45-8:00 | 109.2 |
The per day time deviation of 9 sets of equipment is finally obtained, specific as follows:
By compared with per day time deviation threshold value (threshold value 150s), then determining equipment 6, there are unit exception progress
Early warning.
In conclusion according to embodiment method according to detection day data trick-plate identification equipment 4 and number plate identify equipment 6 into
Row unit exception early warning.
Claims (7)
1. a kind of video number plate Data Quality Analysis method, it is characterised in that: identify equipment on-line situation pair based on video number plate
The integrality of device data acquisition is analyzed, be based further on video number plate historical data and vehicle checker device data quality with
And gps data information analyzes data reasonability, carries out automatic early-warning to equipment fault, includes the following steps,
S1, identify that equipment on-line situation analyzes the integrality of devices collect data according to number plate；
S2, identify that the flow information of devices collect data carries out analytical control to the reasonability of the quality of data according to number plate；
S3, the temporal information of GPS data and number plate identification devices collect data with positioning device vehicle is compared, it is real
Existing number plate identifies data time analysis on its rationality；
S4, number plate identification unit exception and automatic early-warning.
2. video number plate Data Quality Analysis method as described in claim 1, it is characterised in that: step S1 specifically,
The online rate analysis of S11, time dimension, based on number plate identification equipment on-line state in statistical time section to equipment constituent parts
Online situation is for statistical analysis in period, if number of segment ratio is lower than online rate threshold value when online, goes to step S4, otherwise
Go to step S12；
The online rate analysis of S12, Spatial Dimension, extracts all number plates in road network system and identifies equipment on-line data, when to constituent parts
Between always online rate is calculated equipment in section, if always online rate is lower than the online threshold value of system equipment to equipment, then it is assumed that number plate identification
There is the system failure and automatic early-warning in system, otherwise goes to S2 step.
3. video number plate Data Quality Analysis method as described in claim 1, it is characterised in that: step S2 specifically,
S21, data traffic vertical analysis is realized by comparing analysis with device history data, the identification of unit interval number plate is set
The data of standby acquisition are compared and analyzed with same period historical data, if vehicle flowrate exceeds unit interval in unit interval
Devices collect data in the unit interval is then determined as suspicious abnormal data, and goes to step by interior historical traffic threshold interval
Rapid S22, otherwise it is assumed that data are normal in the unit interval, if all unit time segment datas normally turn in statistical time section
To step S3；
S22, by identifying at implantation of device point vehicle checker device data mass ratio to realizing that multi-source data flow is horizontal with number plate
It is analyzed to comparing.
4. video number plate Data Quality Analysis method as claimed in claim 3, it is characterised in that: step S22 specifically,
S221, it extracts with the data of point vehicle checker equipment acquisition in the suspicious abnormal data affiliated period, analysis vehicle checker is set
The standby acquisition quality of data, if the data that all devices acquire in the point abnormal time section are suspicious abnormal data, then it is assumed that
There may be time anomalies, go to step S3, otherwise go to next step.
S222, collect all suspicious abnormal datas in statistical time section, if the ratio that suspicious abnormal data amount accounts for total data is greater than
Suspicious data proportion threshold value, goes to step S4, otherwise goes to step S3.
5. video number plate Data Quality Analysis method according to any one of claims 1-4, it is characterised in that: step S3 is specific
For,
S31, the GPS data in road network with vehicle positioning equipment vehicle is extracted, location data is matched in road network, further
Equipment section contingency table is established according to number plate identification device location and road network road section information in road network, extracts each number plate identification
GPS data within the scope of equipment GPS data；
S32, by within the scope of equipment GPS data and number plate identification data compare and analyze, according to number plate identify equipment acquisition
To number plate of vehicle information extraction go out GPS data of the number plate in data area, obtain the average time difference between two numerical value,
If average time difference is greater than threshold value in unit interval, step S4 is gone to, otherwise it is assumed that number plate identification devices collect data is just
Often.
6. video number plate Data Quality Analysis method as claimed in claim 5, it is characterised in that: equipment range in step S32
Average time difference in the unit interval of interior GPS data and number plate identification data, specifically,
In formula, tGPSFor the data time of GPS gathers, tALPRThe time value of data is identified for number plate, n is that number plate identifies equipment range
Inside collect the data volume of GPS data.
7. video number plate Data Quality Analysis method as claimed in claim 6, it is characterised in that: in step S32, be based on unit
The GPS data of more vehicles of approach is to the average time difference in unit interval in timeIt is calculated, it may be assumed thatFormula
Middle N is vehicle number.
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| CN115376315A (en) * | 2022-08-09 | 2022-11-22 | 北京交通发展研究院 | Road network emission accounting-oriented multi-level bayonet quality control method |
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| CN202042051U (en) * | 2011-03-24 | 2011-11-16 | 浙江金之路信息科技有限公司 | Urban traffic intelligent supervision system |
| CN103000028A (en) * | 2011-09-14 | 2013-03-27 | 上海宝康电子控制工程有限公司 | Vehicle registration plate recognition system and method |
| CN105160887A (en) * | 2015-08-26 | 2015-12-16 | 深圳市傲天智能系统有限公司 | Vehicle image processing device and image processing method thereof |
| CN106355924A (en) * | 2016-09-06 | 2017-01-25 | 江苏智通交通科技有限公司 | Traffic data quality monitoring system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110718071A (en) * | 2019-10-25 | 2020-01-21 | 上海眼控科技股份有限公司 | Verification method and device for image acquisition equipment, computer equipment and storage medium |
| CN110718071B (en) * | 2019-10-25 | 2021-09-03 | 上海眼控科技股份有限公司 | Verification method and device for image acquisition equipment, computer equipment and storage medium |
| CN115376315A (en) * | 2022-08-09 | 2022-11-22 | 北京交通发展研究院 | Road network emission accounting-oriented multi-level bayonet quality control method |
| CN115376315B (en) * | 2022-08-09 | 2023-10-24 | 北京交通发展研究院 | Multi-level bayonet quality control method for road network emission accounting |
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