CN111739176B - ETC portal vehicle passing data processing method - Google Patents
ETC portal vehicle passing data processing method Download PDFInfo
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- CN111739176B CN111739176B CN202010840259.2A CN202010840259A CN111739176B CN 111739176 B CN111739176 B CN 111739176B CN 202010840259 A CN202010840259 A CN 202010840259A CN 111739176 B CN111739176 B CN 111739176B
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/02—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points taking into account a variable factor such as distance or time, e.g. for passenger transport, parking systems or car rental systems
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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Abstract
The invention relates to the technical field of traffic information, which collects passing basic data through devices erected on a portal frame and a toll station, and stores the passing data of a passing vehicle after judging whether a license plate of the passing vehicle is legal or not according to the grouping processing of the license plate; the traffic basic data is further judged whether to belong to provincial boundary or ramp station and then stored and processed; judging whether the flow data is a portal or a toll station; newly generating a portal data storage redis; judging whether the passing time is within ten minutes of the original data, and supplementing the portal data storage to the same data if the judgment result is 'yes'; if the judgment result is 'no', the ETC portal vehicle passing data processing method is regarded as a new passing record for one time to generate and update portal data.
Description
Technical Field
The invention relates to the technical field of traffic information, in particular to a vehicle passing data processing method for an ETC portal frame.
Background
Under the existing development trend of gradually canceling national highway provincial toll stations, aiming at comprehensively improving the auditing work efficiency and providing powerful support service for timely auditing and accurate pursuit of road networks, an intelligent auditing system which is unified in the whole province, four-level linkage, open in service and first-class in the whole country is constructed in a new situation, the highway toll order is further standardized, illegal and illegal behaviors are checked and corrected, the clearing of obstacles and the maintenance of site safety operation are guaranteed, and the harmony, order and safety driving environment is guaranteed to be created.
The inspection features in the networking charging mode should be embodied in the advantages of equipment and science, but not in the advantages of manpower, and inspection personnel at all levels should fully utilize the functions of system data approval and path query to timely carry out inspection work. Meanwhile, necessary data required by operation are provided for the center and the outsiders, so that the timeliness and the accuracy of vehicle inspection work are improved, and the overall level of the online charging inspection management work of the highway is practically enhanced.
Aiming at the problems, the method has the advantages that the preprocessing of a large amount of ETC portal vehicle passing data of the existing road network is urgently needed, the processing of the early-stage basic data is very important, the foundation can be laid for efficient, accurate and accurate registering, taking and utilizing of the subsequent passing data, the data value collected by the existing portal can be fully played, and the big data processing provides more substantial help for the highway management and maintenance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the ETC portal vehicle passing data processing method which is reasonable and rapid in data processing flow, comprehensive in consideration of influence factors, reliable in data screening and low in data redundancy.
The following explanations regarding terms involved in the subsequent technical solution presentation are as follows:
redis is an abbreviation of English Remote Dictionary Server, namely Remote Dictionary service, and is a log-type and Key-Value database which supports network, can be based on memory and can also be persistent.
Kafka is an open source stream processing platform, belongs to a high-throughput distributed publish-subscribe messaging system, and can process all action stream data of a consumer in a website.
Flink is an open source stream processing framework that performs arbitrary stream data processing in a parallel and pipelined manner.
key and value are the meanings of key and value, and are commonly used as key value pairs, in which key is a key and value is a value.
The error-plate is the English language of the wrong plate.
In order to achieve the purpose, the invention adopts the following technical scheme.
The ETC portal vehicle passing data processing method specifically comprises the following steps:
step S1: firstly, collecting basic passing data of passing vehicles passing through a portal frame or a toll station by devices erected on a highway through the portal frame and the toll station, remotely receiving the data through a network, carrying out grouping processing according to license plates, judging whether the license plates of the passing vehicles are legal or not, and then entering the subsequent steps according to the judgment result;
step S2: if the license plate is judged to be an illegal license plate in the step S1, storing the wrong license plate into two redis, and respectively setting the cache formats of the two redis, wherein the Key of the first redis is set as year-month-date error-plate, and Value is set as [ wrong license plate 1, wrong license plate 2 ]; setting Key of a second redis as year-month-date error-license plate 1 and Value as [ { error license plate 1 running water 1}, { error license plate 1 running water 2} ], and then terminating the logic flow of the error license plate data;
step S3: if the license plate judged in the step S1 is a legal license plate, storing the vehicle passing redis data, setting Key to be year-month-date + two first digits of the license plate number, and setting Value to be year-month-date + complete license plate number, and then carrying out subsequent step processing;
step S4: further judging whether the basic traffic data of each portal or toll station in the step S1 belong to a provincial boundary or ramp station, if the judgment result of the provincial boundary or ramp station is 'no', continuously judging whether the basic traffic data belong to a real-time blacklist, and if the basic traffic data belong to the blacklist, pushing kafka and executing storage redis processing; if the judgment result is that the storage request does not belong to the blacklist, directly executing storage redis processing;
if the judgment result of the provincial boundary or the ramp station is 'yes', the provincial boundary or the ramp is stored in redis, the key of the provincial boundary or the ramp station is set as a year-month-date + charging unit, Value is set as [ complete license plate number _ body color number ], whether the license plate data is blacklist data or not is further judged, if not, the redis storage processing is directly executed, and if yes, the redis storage processing is executed and kafka is pushed;
step S5: acquiring a number parameter of the acquisition equipment according to the stored data in the step S4, judging whether the flowing water data is a portal or a toll station according to the data type, directly acquiring the portal number if the flowing water data is judged to be the portal, and converting the portal number into the portal number if the flowing water data is judged to be the toll station; judging whether the portal data exist in the redis or not, and carrying out subsequent step processing according to a judgment result;
step S6: if the portal dimension judgment of the step S5 is 'no', generating new portal data, and storing the newly generated portal data; if the portal dimension of the step S5 is judged to be "yes", acquiring data in redis to perform time grouping processing;
step S7: judging whether the passing time of the portal is within ten minutes of the original data or not in the step S6, and supplementing the portal data storage redis to the same data if the judgment result is 'yes'; and if the judgment result is 'no', determining that a new passing record is generated and updating and perfecting the portal data storage redis, and completing the portal vehicle passing data processing.
As a further improvement of the present invention, the step S1 is to determine whether the license plate is legal specifically by determining whether the license plate meets a license plate regular expression which can be set, if so, the license plate is legal, otherwise, the license plate is illegal.
As a further improvement of the present invention, the blacklist data in step S4 is pre-stored in redis, and the key in the storage format is set as a black-plate number, and the value format is a blacklist rank value.
As a further improvement of the invention, the vehicle traffic data of the provincial or ramp station is also subjected to blacklist verification processing, if the traffic data belongs to blacklist data, the flow is added with a blacklist level and then pushed to kafka, the theme is realBlackTopic, and the data is stored in redis for subsequent real-time blacklist vehicle tracking.
As a further improvement of the present invention, the storage format of the blacklist data after the blacklist verification is set to have a key of realBlack and a value of [ blacklist license plate 1, blacklist license plate 2, blacklist license plate n ], where n represents the recorded nth number of the blacklist license plate.
As a further improvement of the present invention, the traffic basic data of step S1 specifically includes portal flow data, portal board flow identification data, toll station flow data, and toll station board flow identification data.
Due to the application of the technical scheme, the technical scheme of the invention has the following beneficial effects: according to the technical scheme, the acquired road traffic basic data comprise portal running water data, portal board running water data or toll station running water data and toll station board running water data, so that a data basis can be provided for the processing of the subsequent steps, the acquired data are more comprehensive and more sufficient, and the beneficial technical effect of improving the data processing reliability is achieved; the technical scheme also has the beneficial technical effects that the interference of subsequent wrong and illegal license plates is reduced and the logic accuracy of judgment is improved by judging the legality of the license plates and storing and processing the judgment data in different ways; according to the technical scheme, the situation that whether the door frame where the vehicle passes belongs to a provincial boundary or a ramp station is judged, and the real-time blacklist data is verified and compared at the same time, so that judgment and analysis of the provincial boundary or exit ramp direction path where the vehicle passes are achieved, and processing of key node information of passing vehicles is improved; the technical scheme also uniformly converts the collecting equipment type portal and the toll station into portal data numbers, improves the uniformity of data types and facilitates the subsequent positioning processing of portal positions and collected information; according to the technical scheme, whether the passing time interval of the vehicle is within ten minutes or not is judged, so that whether the passing record is updated or not is determined, and the technical effects of improving data screening rationality, avoiding the occupation of computing resources by excessive redundant data and improving processing efficiency are achieved.
Drawings
FIG. 1 is a schematic diagram of the overall flow structure of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following reaction schemes and specific examples.
As shown in fig. 1, a method for processing vehicle passage data of an ETC portal includes the following steps: firstly, collecting basic passing data of passing vehicles passing through a portal frame or a toll station by devices erected on a highway through the portal frame and the toll station, remotely receiving the data through a network, carrying out grouping processing according to license plates, judging whether the license plates of the passing vehicles are legal or not, and then entering the subsequent steps according to the judgment result; if the license plate is judged to be an illegal license plate in the step S1, storing the wrong license plate into two redis, and respectively setting the cache formats of the two redis, wherein the Key of the first redis is set as year-month-date error-plate, and Value is set as [ wrong license plate 1, wrong license plate 2 ]; setting Key of a second redis as year-month-date error-license plate 1 and Value as [ { error license plate 1 running water 1}, { error license plate 1 running water 2} ], and then terminating the logic flow of the error license plate data; if the license plate judged in the step S1 is a legal license plate, storing the vehicle passing redis data, setting Key of the vehicle passing redis data as year-month-date + two previous digits of the license plate number, and setting Value of the vehicle passing redis data as year-month-date + complete license plate number, and then carrying out subsequent step processing;
further judging whether the basic traffic data of each portal or toll station in the step S1 belong to a provincial boundary or ramp station, if the judgment result of the provincial boundary or ramp station is 'no', continuously judging whether the basic traffic data belong to a real-time blacklist, and if the basic traffic data belong to the blacklist, pushing kafka and executing storage redis processing; if the judgment result is that the storage request does not belong to the blacklist, directly executing storage redis processing; if the judgment result of the provincial boundary or the ramp station is 'yes', the provincial boundary or the ramp is stored in redis, the key of the provincial boundary or the ramp station is set as a year-month-date + toll collection unit, Value is set as [ complete license plate number _ body color number, n ], whether the license plate data is blacklist data or not is further judged, if not, the redis storage processing is directly executed, if yes, the redis storage processing is executed and kafka is pushed; judging whether data of the dimensionality of the portal exists in the redis or not, and carrying out subsequent step processing according to a judgment result; if the portal dimension judgment of the step S5 is 'no', generating new portal data, and storing the newly generated portal data; if the portal dimension judgment of the step S5 is 'yes', acquiring data in redis to perform time grouping processing;
judging whether the passing time of the portal is within ten minutes of the original data or not in the step S6, and perfecting the portal data storage redis on the same data if the judgment result is 'yes'; and if the judgment result is 'no', determining that a new passing record is generated and updating and perfecting the portal data storage redis, and completing the portal vehicle passing data processing.
And judging whether the license plate is legal specifically by judging whether the license plate meets a preset license plate regular expression, wherein the license plate is legal if the license plate meets the preset license plate regular expression, and the license plate is illegal if the license plate does not meet the preset license plate regular expression. The blacklist data is pre-stored in redis, the key of the storage format is set as a black-license plate, and the value format is a blacklist grade value. And (3) carrying out blacklist verification processing on the vehicle traffic data of the provincial boundary or ramp station, if the vehicle traffic data belongs to blacklist data, adding a blacklist level to the running water, pushing the running water to kafka to obtain the subject of realBlackTopic, and storing the data to redis for real-time tracking of subsequent blacklisted vehicles. The storage format of the blacklist data after the blacklist verification is set to be realBlack as key, and the value of the storage format is [ blacklist license plate 1, blacklist license plate 2,......... and blacklist license plate n ], wherein n represents the recorded nth number of the blacklist. The passage basic data of step S1 specifically includes portal flow data, portal board flow identification data, or toll station flow data, and toll station board flow identification data.
Data Source acquisition
Acquiring a data source from kafka, wherein 4 data types entering the flink calculation are respectively as follows: the dimension of the collected data is based on the portal and the license plate, so that the portal running water, the portal plate running water or the toll station running water and the toll station plate running water can exist simultaneously only for the same portal and the same license plate.
(II) license plate grouping and verification processing
Acquiring a license plate from the acquired data, wherein the format of the license plate is set as SuA 12345_0, the back _0 represents the color, a standard license plate is intercepted at the moment (for example, SuA 12345_0 is intercepted into SuA 12345), and then, the license plate regular expression format verification is carried out by using SuA 12345.
If the license plate regular expression is not satisfied, the license plate regular expression is not subjected to subsequent logic processing, but needs to be cached in 2 redis keys for storing illegal license plate data.
The cache format is as follows:
year-month-day error-plate [ wrong plate 1, wrong plate 2 ];
year-month-day error-wrong license plate 1 [ { wrong license plate 1 flowing 1}, { wrong license plate 1 flowing 2} ];
if the license plate regular expression is met, the whole license plate (SuA 12345_0) is grouped by using the flink, and a specific subsequent calculation logic is entered.
(III) license plate identification redis storage
The current time is obtained in a format of year-month-day, such as 2020-01-01, and then the first 2 digits of the license plate are intercepted to obtain the SuA.
At the moment, a key of 2020-01-01 SuA is obtained by the current time, year, month and day plus the front 2 bits of the license plate.
And a value of 2020-01-01 SuA 12345_0 can be obtained by the previous time, month and day and the complete license plate.
Finally we call redis, pushing this key and value.
If the SuA is stored for the first time in the current day, the storage format of the redis is as follows:
2020-01-01 threA [ threA 12345_0 ].
If sua has been stored this day, e.g. data of sua 45678_0 has been stored before, the storage format of redis at this time is as follows:
2020-01-01 Su A: [ Su A45678_0, Su A12345_0]
Judgment and processing of provincial boundary and ramp station
Firstly, the equipment number of the traffic data is extracted and judged, and whether the traffic data belongs to provincial boundary data or ramp station data is distinguished.
If the judgment result is yes, the information is stored into redis as a key according to the year-month-day and a charging unit, wherein the charging unit is a charging unit of a provincial boundary and a ramp station.
The storage format is as follows:
key 2020-01-01 charging units [ SuA 45678_0, SuA 12345_0 ].
(V) blacklist data
And storing the existing blacklist data into the redis data.
And the storage format is as follows:
black-license plates such as black-threo A45678;
value is the blacklist rating.
When the blacklist is removed, the corresponding key needs to be deleted.
(VI) blacklist validation of provincial and ramp station data
And if the data is provincial data or ramp station data, performing blacklist verification processing. Specifically, according to the license plate calling data, if the data is judged to be a blacklist, the flow is pushed to kafka after the blacklist level is added, the theme is realBlackTopic, and the data is stored in reds and used for real-time tracking of subsequent blacklisted vehicles.
The storage format is as follows:
key:realBlack；
value: [ blacklist license plate 1, n ].
When the blacklist is unliced, the corresponding value needs to be deleted.
(VII) real-time blacklist tracking of non-provincial and ramp station data
If the data is not provincial data or ramp station data, calling redis by using the realBlack as a key to verify whether the license plate exists in the corresponding value, if so, pushing the running water to the kafka with the theme of realBlackTrackTopic, and pushing the kafka real-time blacklist once in the same charging unit.
(eight) portal transit time grouping logic
The current data is stored on the basis of the same door frame and the same license plate.
When the data of the running water or the card identification is obtained for the first time, the data of the passing time (or the time of the shooting) of a portal are also obtained at the same time, and the data of the passing time is used as the reference passing time of the portal.
And then, when new data of the same license plate under the portal is received, if the passing time of the new data is within 10 minutes before and after the reference passing time, for example, (time-10 minutes) < = time < = (time +10 minutes), the new data is regarded as the same data as the stored reference passing time data.
If the data is not within 10 minutes before or after the reference passing time, the data is regarded as new data to be processed, and the situation that the vehicle passes through the portal again on the same day belongs to.
The above is only a specific application example of the present invention, and the protection scope of the present invention is not limited in any way. All the technical solutions formed by equivalent transformation or equivalent replacement fall within the protection scope of the present invention.
Claims (6)
1. The ETC portal vehicle passing data processing method is characterized by comprising the following steps:
step S1: firstly, collecting basic passing data of passing vehicles passing through a portal frame or a toll station by devices erected on a highway through the portal frame and the toll station, remotely receiving the data through a network, carrying out grouping processing according to license plates, judging whether the license plates of the passing vehicles are legal or not, and then entering the subsequent steps according to the judgment result;
step S2: if the license plate is judged to be an illegal license plate in the step S1, storing the wrong license plate into two redis, and respectively setting the cache formats of the two redis, wherein the Key of the first redis is set as year-month-date error-plate, and Value is set as [ wrong license plate 1, wrong license plate 2 ]; setting Key of a second redis as year-month-date error-license plate 1 and Value as [ { error license plate 1 running water 1}, { error license plate 1 running water 2} ], and then terminating the logic flow of the error license plate data;
step S3: if the license plate judged in the step S1 is a legal license plate, storing the vehicle passing redis data, setting Key to be year-month-date + two first digits of the license plate number, and setting Value to be year-month-date + complete license plate number, and then carrying out subsequent step processing;
step S4: further judging whether the basic traffic data of each portal or toll station in the step S1 belong to a provincial boundary or ramp station, if the judgment result of the provincial boundary or ramp station is 'no', continuously judging whether the basic traffic data belong to a real-time blacklist, and if the basic traffic data belong to the blacklist, pushing kafka and executing storage redis processing; if the judgment result is that the storage request does not belong to the blacklist, directly executing storage redis processing;
if the judgment result of the provincial boundary or the ramp station is 'yes', the provincial boundary or the ramp station is stored in redis, the key of the provincial boundary or the ramp station is set as a year-month-date + charging unit, Value is set as [ complete license plate number _ body color number ], whether the license plate data is blacklist data or not is further judged, if not, the redis storage processing is directly executed, and if yes, the redis storage processing is executed and kafka is pushed;
step S5: acquiring a number parameter of the acquisition equipment according to the stored data in the step S4, judging whether the flowing water data is a portal or a toll station according to the data type, directly acquiring the portal number if the flowing water data is judged to be the portal, and converting the portal number into the portal number if the flowing water data is judged to be the toll station; judging whether data of the dimensionality of the portal exists in the redis or not, and carrying out subsequent step processing according to a judgment result;
step S6: if the portal dimension judgment of the step S5 is 'no', generating new portal data, and storing the newly generated portal data; if the portal dimension of the step S5 is judged to be "yes", acquiring data in redis to perform time grouping processing;
step S7: judging whether the passing time of the portal is within ten minutes of the original data or not in the step S6, and supplementing the portal data storage redis to the same data if the judgment result is 'yes'; and if the judgment result is 'no', determining that a new passing record is generated and updating and perfecting the portal data storage redis, and completing the portal vehicle passing data processing.
2. The ETC portal vehicle passage data processing method according to claim 1, wherein: the step S1 is to determine whether the license plate is legal specifically by determining whether the license plate satisfies a license plate regular expression that can be set, if so, the license plate is legal, otherwise, the license plate is illegal.
3. The ETC portal vehicle passage data processing method according to claim 1, wherein: the blacklist data in the step S4 is pre-stored in redis, and the key in the storage format is set as a black-plate number, and the value format is a blacklist rank value.
4. The ETC portal vehicle passage data processing method according to claim 1, wherein: and carrying out blacklist verification processing on the vehicle passing data of the provincial boundary or the ramp station, if the vehicle passing data belongs to blacklist data, adding a blacklist level to the running water, pushing the running water to kafka to obtain the data with the theme of realBlackTopic, and storing the data into redis for real-time tracking of subsequent blacklisted vehicles.
5. The ETC portal vehicle passage data processing method according to claim 4, wherein: the storage format of the blacklist data after the blacklist verification is set to be that key is realBlack, and value is [ blacklist license plate 1, blacklist license plate 2, blacklist license plate n ], wherein n represents the recorded nth number of the blacklist license plate.
6. The ETC portal vehicle passage data processing method according to claim 1, wherein: the passage basic data of step S1 specifically includes portal flow data, portal board flow identification data, or toll station flow data, and toll station board flow identification data.
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| CN105630913A (en) * | 2015-12-22 | 2016-06-01 | 福建星海通信科技有限公司 | Redis pipeline communication-based message passing method and system |
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