CN111179603A - Vehicle identification method and device, electronic equipment and storage medium - Google Patents
Vehicle identification method and device, electronic equipment and storage medium Download PDFInfo
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- CN111179603A CN111179603A CN201811330662.XA CN201811330662A CN111179603A CN 111179603 A CN111179603 A CN 111179603A CN 201811330662 A CN201811330662 A CN 201811330662A CN 111179603 A CN111179603 A CN 111179603A
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Abstract
The embodiment of the invention provides a vehicle identification method and device, electronic equipment and a storage medium. The method comprises the following steps: determining vehicle passing data of a target vehicle in a target time period; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle; and judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate. The embodiment of the invention can effectively identify the vehicle with the abnormal license plate.
Description
Technical Field
The present invention relates to the field of transportation, and in particular, to a vehicle identification method and apparatus, an electronic device, and a storage medium.
Background
The license plate is a vehicle number plate, is a number of each vehicle, and has the main functions that the province, the city and the county of the vehicle can be known through the license plate, and the owner of the vehicle can be found by a vehicle management station according to the license plate.
In real life, vehicles with abnormal license plates, such as license plate changing vehicles and license plate sleeving vehicles, often appear on roads, and normal traffic order is influenced. The license plate replacing vehicle uses license plates of other vehicles; the fake-licensed car is also called a cloned car, and fake cards with the same number are sleeved on license plates with the same model and color according to the model and color of a real-licensed car.
Therefore, how to effectively identify the vehicle with the abnormal license plate is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention aims to provide a vehicle identification method, a vehicle identification device, electronic equipment and a storage medium, so as to effectively identify a vehicle with an abnormal license plate. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a vehicle identification method, where the method includes:
determining vehicle passing data of a target vehicle in a target time period; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle;
and judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate.
Optionally, the determining the vehicle passing data of the target vehicle in the target time period includes:
and determining the passing data of the target vehicle in the target time period from the pre-stored passing data of the target vehicle in each time period.
Optionally, the time periods include: each date field;
the storage mode of the passing data of the target vehicle in each time period comprises the following steps: storing the passing data of the target vehicle in each date section by taking each date as a storage keyword;
wherein, for each date, there are N sets of vehicle passing data, of which a first set represents vehicle passing data within a date section constituted by the current date of the target vehicle and an nth set represents vehicle passing data within a date section constituted by the current date of the target vehicle and previous N-1 days; wherein N is a natural number greater than 1.
Optionally, the determining the vehicle passing data of the target vehicle in the target time period from the pre-stored vehicle passing data of the target vehicle in each time period includes:
determining an end date of the target date field;
determining multiple groups of vehicle passing data with storage keywords as the termination dates from pre-stored vehicle passing data of target vehicles in each date field;
determining the number of days occupied by the target date field;
and determining a group of the passing data with the arrangement number of the passing data group being the number of days in the determined multiple groups of the passing data to obtain the passing data of the target vehicle in the target date field.
Optionally, the process of storing the N sets of vehicle passing data on any date includes:
determining the vehicle passing data of the target vehicle in the current day of the date, and taking the vehicle passing data in the current day as a first set of vehicle passing data of the date;
acquiring pre-stored front N-1 groups of vehicle passing data of the target vehicle on the day before the date;
adding the group of vehicle passing data and the first group of vehicle passing data of the date to obtain N-1 groups of vehicle passing data of the date aiming at each group of vehicle passing data in the previous N-1 groups of vehicle passing data of the previous day;
and sequentially storing the first group of vehicle passing data on the date and the N-1 groups of vehicle passing data on the date as the N groups of vehicle passing data on the date.
Optionally, the determining the vehicle passing data of the target vehicle within the current day includes:
determining the checkpoint information of the reference area and the driving record of the target vehicle in the current day;
and determining the passing data of the target vehicle in the current day based on the checkpoint information and the driving record.
Optionally, the determining whether the target vehicle meets a preset abnormal license plate condition based on the number of entering times and the number of leaving times includes:
calculating a difference between the number of the entering times and the number of the leaving times;
and judging whether the calculated time difference is larger than a first preset threshold value or not, and if so, judging that the target vehicle meets a preset abnormal license plate condition.
Optionally, the vehicle passing data further includes the total number of times in the domain; wherein the total number of times in the area is the total number of times of passing the target vehicle in the reference area;
the judging whether the target vehicle meets the preset abnormal license plate condition or not based on the entering times and the leaving times comprises the following steps:
summing the entering times and the leaving times to obtain the total entering and exiting times of the target vehicle in the reference area;
calculating the difference between the total number of the accesses and the total number of the accesses in the domain;
and judging whether the calculated time difference is larger than a second threshold value, and if so, judging that the passing data meets the preset abnormal license plate condition.
In a second aspect, an embodiment of the present invention provides a vehicle identification apparatus, including:
the determining module is used for determining vehicle passing data of the target vehicle in the target time period; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle;
and the judging module is used for judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate.
Optionally, the determining module is specifically configured to:
and determining the passing data of the target vehicle in the target time period from the pre-stored passing data of the target vehicle in each time period.
Optionally, the time periods include: each date field;
the storage mode of the passing data of the target vehicle in each time period comprises the following steps: taking each date as a storage keyword, and storing the passing data of the target vehicle in each date section by a storage module;
wherein, for each date, there are N sets of vehicle passing data, of which a first set represents vehicle passing data within a date section constituted by the current date of the target vehicle and an nth set represents vehicle passing data within a date section constituted by the current date of the target vehicle and previous N-1 days; wherein N is a natural number greater than 1.
Optionally, the determining module includes:
a first determining submodule for determining an end date of the target date field;
the second determining submodule is used for determining a plurality of groups of vehicle passing data with storage keywords as the termination dates from the vehicle passing data of the target vehicle in each date field pre-stored in the storage module;
a third determining submodule for determining the number of days occupied by the target date field;
and the fourth determining submodule is used for determining a group of the vehicle passing data with the arrangement number of the vehicle passing data group being the number of days in the determined groups of the vehicle passing data to obtain the vehicle passing data of the target vehicle in the target date field.
Optionally, the storage module is configured to store N sets of vehicle passing data on any date, and the storage module includes:
the fifth determining submodule is used for determining the vehicle passing data of the target vehicle in the current day on the date and taking the vehicle passing data in the current day as the first group of vehicle passing data on the date;
the acquisition submodule is used for acquiring pre-stored front N-1 groups of vehicle passing data of the target vehicle on the day before the date;
the calculation module is used for adding the group of vehicle passing data and the first group of vehicle passing data of the date to obtain N-1 groups of vehicle passing data of the date aiming at each group of vehicle passing data in the previous N-1 groups of vehicle passing data of the previous day;
and the storage submodule is used for sequentially storing the first group of vehicle passing data on the date and the N-1 groups of vehicle passing data on the date as the N groups of vehicle passing data on the date.
Optionally, the fifth determining submodule is specifically configured to:
determining the checkpoint information of the reference area and the driving record of the target vehicle in the current day;
and determining the passing data of the target vehicle in the current day based on the checkpoint information and the driving record.
Optionally, the determining module is specifically configured to:
calculating a difference between the number of the entering times and the number of the leaving times;
and judging whether the calculated time difference is larger than a first threshold value, and if so, judging that the target vehicle meets a preset abnormal license plate condition.
Optionally, the vehicle passing data further includes the total number of times in the domain; wherein the total number of times in the area is the total number of times of passing the target vehicle in the reference area;
the judgment module is specifically configured to:
summing the entering times and the leaving times to obtain the total entering and exiting times of the target vehicle in the reference area;
calculating the difference between the total number of the accesses and the total number of the accesses in the domain;
and judging whether the calculated time difference is larger than a second threshold value, and if so, judging that the passing data meets the preset abnormal license plate condition.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, wherein,
the memory is used for storing a computer program;
the processor is configured to implement the steps of the vehicle identification method provided by the embodiment of the present invention when executing the program stored in the memory.
In a fourth aspect, the embodiment of the invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the vehicle identification method provided by the embodiment of the invention.
In the scheme provided by the embodiment of the invention, the times that a vehicle enters and leaves a preset reference area in a period of time are considered to be similar, and if the deviation of the two times is larger, the possibility that the corresponding vehicle is a vehicle with an abnormal license plate is higher. Therefore, based on the condition, the abnormal license plate condition can be preset, and when whether a target vehicle is a vehicle with an abnormal license plate is judged, vehicle passing data of the target vehicle in a target time period is determined firstly; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle; and then, judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate. Therefore, the purpose of effectively identifying the vehicle with the abnormal license plate can be achieved by applying the embodiment of the invention.
Of course, it is not necessary for any product or method of practicing the invention to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a vehicle identification method according to an embodiment of the present invention;
FIG. 2 is a timing diagram of the present invention for storing HBASE statistics;
fig. 3 is a schematic structural diagram of a vehicle identification device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to achieve the purpose of effectively identifying a vehicle with an abnormal license plate, the embodiment of the invention provides a vehicle identification method, a vehicle identification device, electronic equipment and a storage medium.
It should be noted that the execution subject of the vehicle identification method provided by the embodiment of the present invention may be a vehicle identification apparatus, and the apparatus may be operated in an electronic device, and the electronic device may be a server or a terminal, and of course, the electronic device is not limited thereto.
First, a vehicle identification method according to an embodiment of the present invention will be described.
As shown in fig. 1, a vehicle identification method provided in an embodiment of the present invention may include the following steps:
s101, determining vehicle passing data of a target vehicle in a target time period;
in the embodiment of the present invention, in order to obtain an effective determination result, the target time period may include multiple days, for example, the target time period may be a week, a month, or a half year; the target time period can also be a date period, such as 1/2018 to 2/1/2018; of course, the target time period may have a certain time point, such as 9 am earlier by 1 month 1 of 2018 to 9 am later by 1 month 1 of 2018, and the like, which is reasonable.
In an embodiment of the present invention, the vehicle passing data includes: and aiming at a preset reference area, the number of times of entering and the number of times of leaving of the target vehicle.
The preset reference area is an area having a certain geographical range, for example, the preset reference area may be an urban area, a city, or a province, etc. For convenience of description, in the present application, the preset reference area is taken as an example of a city for explanation.
In practice, in order to achieve the purposes of monitoring, traffic control and the like, various bayonets are distributed in cities, for example, a plurality of bayonets in cities are arranged in cities, and a plurality of bayonets between the cities and other cities are arranged at junctions of the cities. Entering an intercity checkpoint represents entering the city and leaving an intercity checkpoint represents exiting the city.
Then, for the preset reference area being a city, the vehicle passing data may include: the number of times of entering the city and the number of times of leaving the city of the target vehicle are the number of times of entering the city and the number of times of leaving the city.
The process of determining the number of times of entering the city and the number of times of leaving the city of the target vehicle can be that the target vehicle is recorded when passing through each intercity gate of the city, so that the number of times of entering the city and the number of times of leaving the city of the target vehicle are obtained through statistics; the recording mode can be manual recording, card swiping recording and the like.
As an alternative, the process of determining the number of entering cities and the number of exiting cities of the target vehicle may be: the method comprises the steps of collecting images of vehicles passing through each intercity bayonet by using video monitoring equipment such as a camera arranged at each intercity bayonet of a city, carrying out license plate recognition on the collected images of the vehicles to obtain license plate numbers, and obtaining the number of times of entering the city and the number of times of leaving the city of the vehicles corresponding to each license plate number based on the number of the collected images with unified license plate numbers, so as to obtain the number of times of entering the city and the number of times of leaving the city of a certain target vehicle.
It is understood that, for the reference area outside the city, the determination process of the number of entering times and the number of leaving times of the target vehicle is similar to the determination process of the city, and is not described herein again.
Certainly, since a plurality of urban bayonets are also distributed in a city, optionally, the vehicle passing data may further include the total number of times in the domain; and the total number of times in the area is the total number of times of passing the target vehicle in the reference area. And aiming at the preset reference area as the city, the total times in the domain are the total times of the target vehicle passing through the city, namely the total times of the target vehicle passing through each gate in the city.
In the embodiment of the invention, images shot by video monitoring equipment of each gate in city in a target time period can be acquired, and then the vehicle passing data of the target vehicle is determined based on the acquired images.
Optionally, in order to improve the efficiency of vehicle identification, in the embodiment of the present invention, vehicle passing data of each vehicle in each time period may be stored in advance for each vehicle; when the target vehicle needs to be identified, the vehicle passing data of the target vehicle in the target time period is determined from the pre-stored vehicle passing data of the target vehicle in each time period.
For clarity of layout and ease of understanding of the solution, the process of storing the passing data of the target vehicle in each time period will be described later.
And S102, judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate.
In combination with the reality, the entering frequency and the leaving frequency of a vehicle should be similar in a period of time; if the number of times of coming into the city and the number of times of going out of the city are greatly different in a period of time, the vehicle is possibly a vehicle with an abnormal license plate, such as a card changing vehicle or a card registering vehicle.
Therefore, in the embodiment of the present invention, an abnormal license plate condition may be preset based on the analysis, for example: setting a first threshold value for a difference between the number of entries and the number of departures. Wherein the first threshold value can be determined by analyzing big data of the vehicle.
Then, step S102 may include step a1 and step a 2:
a step a1 of calculating a difference between the number of the entering times and the number of the leaving times;
step a2, judging whether the calculated time difference is larger than a first threshold value, if so, judging that the target vehicle meets the preset abnormal license plate condition.
For example, for a preset reference area, when the preset reference area is a city, a difference value between the number of times of entering the city and the number of times of leaving the city may be calculated, and it is determined whether the calculated difference value is greater than a first threshold value, if so, it indicates that the difference between the number of times of entering the city and the number of times of leaving the city is greater in a target time period, and then it may be determined that the target vehicle meets a preset abnormal license plate condition, where the target vehicle is a vehicle with an abnormal license plate, such as a license plate changing vehicle or a license plate sleeving vehicle.
Of course, another abnormal license plate condition may also be preset, such as: setting a threshold value for the difference between the leaving times and the entering times. And calculating the difference value of the leaving times and the entering times, judging whether the calculated difference value of the times is greater than the threshold value, and if so, judging that the target vehicle meets the preset abnormal license plate condition. The threshold may be the same as or different from the first threshold.
In the embodiment of the present invention, the vehicle passing data may further include total times in a domain, and for a preset reference region being a city, the total times in the domain, that is, the total times of the target vehicle passing through each gate in the city, may be referred to as total times in the city for short.
In addition, according to the fact that the number of the urban bayonets in a city is far more than that of the intercity bayonets in general, the total times of a vehicle passing through each urban bayonets in the city in a period of time is far more than that of the vehicle entering and leaving the city; if the total number of times that a vehicle passes through the gates in each city in a period of time is far less than the total number of times that the vehicle enters the city and leaves the city, the vehicle may be a vehicle with an abnormal license plate, such as a card changing vehicle or a card sleeving vehicle.
Therefore, in the embodiment of the present invention, another abnormal license plate condition may be preset based on the above analysis, for example: and setting a second threshold value for the difference value between the total in-out times and the total in-domain times. The second threshold value may also be determined by analyzing the big data of the vehicle.
Then, step S102 may include step b1 through step b 3:
step b1, summing the entering times and the leaving times to obtain the total entering and exiting times of the target vehicle in the reference area;
step b2, calculating the difference between the total number of the in-out times and the total number of the in-domain times;
and b3, judging whether the calculated time difference is larger than a second threshold value, and if so, judging that the passing data meets the preset abnormal license plate condition.
For example, for a preset reference area being a city, the number of entering the city and the number of exiting the city may be summed up to obtain the total number of entering and exiting the city by the target vehicle; a difference between the total number of entrances and exits and the total number of cities may be calculated.
It can be understood that, if the number difference is greater than the second threshold, it indicates that the total number of times that the target vehicle passes through each gate in a city in a target time period is far less than the total number of times that the vehicle enters and leaves the city, then it can be determined that the target vehicle meets a preset abnormal license plate condition, and the target vehicle is a vehicle with an abnormal license plate, such as a license plate changing vehicle or a license plate sleeving vehicle.
It should be noted that, if the vehicle passing data includes the number of entering times, the number of leaving times, and the total number of times in the domain, the determination may be performed by using any one of the above-mentioned multiple abnormal license plate conditions.
In the scheme provided by the embodiment of the invention, the times that a vehicle enters and leaves a preset reference area in a period of time are considered to be similar, and if the deviation of the two times is larger, the possibility that the corresponding vehicle is a vehicle with an abnormal license plate is higher. Therefore, based on the condition, the abnormal license plate condition can be preset, and when whether a target vehicle is a vehicle with an abnormal license plate is judged, vehicle passing data of the target vehicle in a target time period is determined firstly; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle; and then, judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate. Therefore, the purpose of effectively identifying the vehicle with the abnormal license plate can be achieved by applying the embodiment of the invention.
The following describes a process of storing vehicle passing data of a vehicle in each time period:
in the embodiment of the invention, the storage system HBASE can be utilized to store a plurality of vehicle passing data of each vehicle on each date in the corresponding HBASE statistical table, so that the HBASE statistical table corresponding to each vehicle is obtained. Among them, HBASE is a highly reliable, high performance, nematic, scalable distributed storage system.
Specifically, for a target vehicle, the vehicle passing data of the target vehicle in each date section can be stored by taking each date as a storage key word;
wherein, for each date, there are N sets of vehicle passing data, of which a first set represents vehicle passing data within a date section constituted by the current date of the target vehicle and an nth set represents vehicle passing data within a date section constituted by the current date of the target vehicle and previous N-1 days; wherein N is a natural number greater than 1. N may be set according to requirements, such as 7 or 15, and so on.
In the embodiment of the invention, HBASE can also be used for storing a plurality of vehicle passing data of a plurality of vehicles on each date in one HBASE statistical table. Specifically, each date, license plate number and license plate color can be used as a storage key to store the vehicle passing data of each vehicle in each date section.
First, taking the second storage form as an example, the stored form of the HBASE statistical table is intuitively understood in conjunction with Table 1, see Table 1, where Table 1 is an exemplary HBASE statistical table.
The passing data of a target vehicle at each date stage is shown in table 1. Wherein, the past-placeno-placeresolver is a row keyword, i.e. a storage keyword. passtime is the date, placeno is the number of the vehicle and plastecolor is the color of the number plate. Such as a row of keys that may be 20180101-xyz 000-blue, etc. cf is a column family name which can be characters such as numbers; for the convenience of inquiry, the column names corresponding to different dates are different for the same vehicle. pn1, ic1, oc1, and the like are column names, and column names with different arrangement numbers correspond to different passing data sets.
TABLE 1
Specifically, table 1 includes 32 sets of passing data corresponding to the dates indicated in the row key. The 1 st group of passing data includes: total number of times of day within city (pn1), number of times of day within city (ic1), and number of times of day out of city (oc 1);
the 2 nd group of passing data includes: total number of urban entries within 2 days (pn2), number of urban entries within 2 days (ic2) and number of urban exits within 2 days (oc 2); wherein the total number of times within 2 days indicates the total number of times within the city of the target vehicle within a date section constituted by the current day and 1 day before the current day, the number of times within 2 days indicates the number of times within the city of the target vehicle within a date section constituted by the current day and 1 day before the current day, and the number of times within 2 days indicates the number of times within the date section constituted by the current day and 1 day before the current day.
By analogy, the 32 nd group of passing data includes: total number of urban entries within 32 days (pn32), number of urban entries within 32 days (ic32) and number of urban exits within 32 days (oc 32); the total number of times of coming-in within 32 days represents the total number of times of coming-in within the date section formed by the current day and 31 days before the current day of the target vehicle, the number of times of coming-in within 32 days represents the number of times of coming-in within the date section formed by the current day and 31 days before the current day of the target vehicle, and the number of times of going-out within 32 days represents the number of times of going-out within the date section formed by the current day and 31 days before the current day of the target vehicle. It can be seen that the 32 nd group of vehicle passing data represents vehicle passing data in a date section formed by the current day and the previous month.
It can be seen that in table 1, the different past vehicle data sets represent different dates. In the embodiment of the invention, the vehicle passing data in the plurality of date sections are stored for the target vehicle, so that the selection of the plurality of date sections can be provided for the target time section when the target vehicle is identified in the follow-up process, and the efficiency of data query is improved.
Secondly, the storage process of the vehicle passing data is explained by combining the HBASE statistical table:
it will be appreciated that for each target vehicle, if the target vehicle has been present within the last month, then every day during that month, passing data like table 1 will be stored for that target vehicle. The storage process of N sets of passing data on any date for a target vehicle may include steps c1 to c 4:
step c1, determining the passing data of the target vehicle in the current day on the date, and using the passing data in the current day as the first group of passing data on the date;
specifically, step c1 may include step c11 and step c 12:
step c11, determining the checkpoint information of the reference area and the driving record of the target vehicle in the current day;
step c12 determines the passing data of the target vehicle in the current day based on the checkpoint information and the driving record.
Step c2, acquiring pre-stored front N-1 groups of passing data of the target vehicle on the day before the date;
step c3, adding the group of vehicle passing data and the first group of vehicle passing data of the date to each group of vehicle passing data in the previous N-1 groups of vehicle passing data of the previous day to obtain N-1 groups of vehicle passing data of the date;
and step c4, sequentially storing the first group of vehicle passing data on the date and the N-1 groups of vehicle passing data on the date as the N groups of vehicle passing data on the date.
For ease of understanding, assume that N is 3, the date to be stored is 20180202, the license plate number of the target vehicle is xyz000, and the license plate color is blue. The above process is illustrated in conjunction with figure 2, figure 2 is a timing diagram of the present invention for storing the HBASE statistics.
With respect to step c1, first, the vehicle identification device may invoke the calculation engine SPARK to start a timing task; among them, SPARK is a fast and general-purpose distributed computing engine designed specifically for large-scale data processing.
Specifically, the vehicle identification device may invoke a SPARK to start a timing task at 24 nights of 20180202, and the SPARK acquires, from a PostgreSQL database in which bayonet information is stored, bayonet information of a city, which includes types of each bayonet of the city, and caches the bayonet information in a memory corresponding to the SPARK, where the bayonet information includes, for example, a bayonet 1 is an intercity bayonet, a bayonet 2 is an intercity bayonet, and the like.
Meanwhile, the SPARK can acquire the driving record of the target vehicle in 20180202 days from the HBASE database. The HBASE database can pre-store the daily vehicle driving records in the form of an HBASE record table, and the HBASE record table can store the name and the driving direction of a passing gate of each vehicle by taking time, license plate number and license plate color as keywords. For example, it can record 9 am, the number plate number is 123, the vehicle 1 with the white color number plate passes through the gate 1, the driving direction is from south to north, and so on.
It is understood that the driving record may be obtained by analyzing the images captured by the video monitoring devices of the respective checkpoints, such as an image analysis method, and the determination process of the driving record is not the invention of the present application and will not be described in detail herein.
Then, the SPARK may determine, based on the checkpoint information and the driving record of the target vehicle in 20180202 the day, the data of the target vehicle passing through the day in the day, and use the data of the current day as 20180202 first group of data of passing through the vehicle.
Specifically, it can be known from the gate information that gate 1 is an intercity gate, and it can be determined from the position of gate 1 that the driving direction is entering the city from south to north, so that the entering frequency of the target vehicle in 20180202 the day can be determined to be 1, and similarly, the entering frequency and the total number of times of the target vehicle in 20180202 the day can be obtained.
For example, after step c1, the SPARK may determine that the passing data of the target vehicle within 20180202 days is: the total number of times of urban entering is 0, the number of times of urban entering is 1, the number of times of urban exiting is 1, and the data of passing vehicles in the current day is used as 20180202-th group data of passing vehicles.
It should be noted that, in order not to affect the use of the database by other users and to increase the subsequent data processing speed, at a time point in the morning of 20180203, the checkpoint information of the city and the driving record of the target vehicle in the day 20180202 may be determined, and the passing data of the target vehicle in the day 20180202 may be determined based on the checkpoint information and the driving record.
For step c2, SPARK may retrieve pre-stored first 2 sets of passing data from the HBASE database for the target vehicle on the day before 20180202.
Specifically, a prestored HBASE statistical table can be obtained from the HBASE database, and the front 2 groups of passing data of the target vehicle at 20180201 are obtained according to the storage keyword of 20180201-xyz 000-blue. In order to keep the number of the passing data sets uniform, the oldest 3 rd group of passing data is discarded for the 3 groups of passing data of 20180201, so that the preceding 2 groups of passing data of 20180201 and the passing data of the current day of 20180202 form 3 groups of passing data of 20180202.
For example, the front 2 sets of vehicle passing data obtained at 20180201 by the target vehicle are respectively: 20180201 the total number of times of entering city is 1, the number of times of entering city is 0, and the number of times of leaving city is 0; 20180201 the total number of times of entering city is 5, the number of times of entering city is 2, and the number of times of leaving city is 2.
For step c3, for each of the first 2 sets of passing data of 20180201, the SPARK adds the set of passing data to the 1 st set of passing data of 20180202 to obtain 2 sets of passing data of 20180202.
It can be understood that, in practice, there may be three cases of passing data two days before and after a target vehicle, the target vehicle having the passing data on the first day and having no passing data on the second day; or the target vehicle has no vehicle passing data in the first day and has vehicle passing data in the second day; or the target vehicle has vehicle passing data on both the first day and the second day. Therefore, when the addition operation is performed, the number of times of addition obtained does not change from before with respect to the second case.
Specifically, group 1 passing data of 20180201 may be: total number of urban entries is 1, number of urban entries is 0, number of urban exits is 0, and 20180202, group 1 vehicle passing data: the total number of times of urban entering is 0, the number of times of urban entering is 1, and the number of times of urban exiting is 1, and the sum is added to obtain 20180202, wherein the data of the group 2 of passing vehicles is: the total number of times of entering the city is 1, the number of times of entering the city is 1, and the number of times of leaving the city is 1;
group 2 passing data of 20180201: total number of urban entries is 5, number of urban entries is 2, number of urban exits is 2, and 20180202, group 1 vehicle passing data: the total number of times of urban entering is 0, the number of times of urban entering is 1, and the number of times of urban exiting is 1, and the total number of times of urban entering is added to obtain 20180202, wherein the 3 rd group passing data is as follows: the total number of times of entering the city is 5, the number of times of entering the city is 3, and the number of times of leaving the city is 3.
For step c4, SPARK sequentially stores 20180202 of the 1 st set of passing data and 20180202 of the 2 sets of passing data as 20180202 of the 3 sets of passing data.
Specifically, the SPARK may sequentially send 20180202 sets of passing data to the HBASE database, and the HBASE database stores the 3 sets of passing data in the HBASE statistical table in a storage manner shown in table 1 by using a keyword 20180202-xyz 000-blue.
It should be noted that after the HBASE database is stored, a stored message may be sent to the SPARK, and then the SPARK sends a message to the vehicle identification device to end the timing task. At this point, the storage task of the N sets of passing data indicating that the target vehicle is on that date is completed.
In the embodiment of the invention, the statistical result of the vehicle passing data of the last 32 days is stored for each vehicle, and each time the vehicle is identified, the current-day vehicle passing data of the target vehicle only needs to be acquired, and the current-day process data and the previous-day vehicle passing data are added, so that the calculation amount can be reduced, and the performance of the timing task can be improved.
It should be added that, when the HBASE statistical table is stored for the first time, for each vehicle, the vehicle passing data of the vehicle in the current day may be stored as 32 sets of vehicle passing data, that is, the vehicle passing data of the 1 st to 32 nd sets are all the vehicle passing data of the vehicle in the 1 st day;
on the 2 nd day, storing the 1 st group of vehicle passing data as the vehicle passing data in the 2 nd day aiming at the vehicle, adding the vehicle passing data in the 2 nd day with the vehicle passing data in the previous 1 st day, and storing the result obtained by the addition as the vehicle passing data of the 2 nd group to the 32 nd group;
on the 3 rd day, storing the 1 st group of vehicle passing data as the vehicle passing data in the 3 rd day aiming at the vehicle, adding the vehicle passing data in the 3 rd day with the vehicle passing data in the previous 1 th day, and storing the result obtained by the addition as the 2 nd group of vehicle passing data; adding the vehicle passing data in the day 3 with the vehicle passing data in the previous 2 days, and storing the result obtained by the addition as the vehicle passing data of the 3 rd to the 32 th groups;
by analogy, after 32 days of execution, an HBASE statistical table different from each group of vehicle passing data can be obtained.
In the above description of the storage process of the hbsase statistical table, in this embodiment of the present invention, the determining the vehicle-passing data of the target vehicle in the target time period from the pre-stored vehicle-passing data of the target vehicle in each time period may include steps d1 to d 4:
step d1, determining the end date of the target date field;
step d2, determining multiple sets of passing data with storage keywords as the ending dates from the pre-stored passing data of the target vehicle in each date field;
step d3, determining the number of days taken by the target date field;
and d4, determining a group of the passing data with the arrangement number of the passing data group being the number of days in the determined groups of the passing data, and obtaining the passing data of the target vehicle in the target date field.
For ease of understanding, the above steps are exemplified in conjunction with table 1. For example, the license plate number of the target vehicle is xyz000, the color of the license plate is blue, and the target date section is 20180701-20180715.
First, it can be determined that the end date of the date field is 20180715, and the number of days taken by 20180701-20180715 is 15;
then, in the HBASE statistical table, 32 sets of passing data with a row key of 20180715 are queried. Since the nth group of passing data represents passing data of the target vehicle in a date band formed by N-1 days before and on the current day 20180715, it can be understood that the 15 th group of passing data represents passing data of the target vehicle in a date band formed by 20180715 days before and on the 14 days before and on the 3580701-20180715, that is, passing data in a date band corresponding to 20180701-20180715, the 15 th group of passing data can be obtained from the 32 groups of passing data of 20180715, that is, passing data of the target vehicle in 20180701-20180715 can be obtained.
In the embodiment of the present invention, the keyword 20180715, cf: the pn15, ic15 and oc15 inquire corresponding passing data.
Therefore, when the vehicle is identified, the prestored HBASE statistical table is used, and only the keywords are needed to be used for acquiring the data of the corresponding column, so that the vehicle passing data of the target vehicle in any date section can be conveniently and quickly inquired, the data inquiry performance can be improved, and the vehicle identification efficiency can be improved.
Corresponding to the above method embodiment, an embodiment of the present invention further provides a vehicle identification apparatus, as shown in fig. 3, the apparatus including:
the determining module 301 is used for determining vehicle passing data of the target vehicle in a target time period; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle;
the determining module 302 is configured to determine whether the target vehicle meets a preset abnormal license plate condition based on the number of entering times and the number of leaving times, and if yes, determine that the target vehicle is a vehicle with an abnormal license plate.
Optionally, in this embodiment of the present invention, the determining module 301 is specifically configured to:
and determining the passing data of the target vehicle in the target time period from the pre-stored passing data of the target vehicle in each time period.
Optionally, in an embodiment of the present invention, each time period includes: each date field;
the storage mode of the passing data of the target vehicle in each time period comprises the following steps: taking each date as a storage keyword, and storing the passing data of the target vehicle in each date section by a storage module;
wherein, for each date, there are N sets of vehicle passing data, of which a first set represents vehicle passing data within a date section constituted by the current date of the target vehicle and an nth set represents vehicle passing data within a date section constituted by the current date of the target vehicle and previous N-1 days; wherein N is a natural number greater than 1.
Optionally, in this embodiment of the present invention, the determining module 301 includes:
a first determining submodule for determining an end date of the target date field;
the second determining submodule is used for determining a plurality of groups of vehicle passing data with storage keywords as the termination dates from the vehicle passing data of the target vehicle in each date field pre-stored in the storage module;
a third determining submodule for determining the number of days occupied by the target date field;
and the fourth determining submodule is used for determining a group of the vehicle passing data with the arrangement number of the vehicle passing data group being the number of days in the determined groups of the vehicle passing data to obtain the vehicle passing data of the target vehicle in the target date field.
Optionally, in an embodiment of the present invention, the storage module is configured to store N sets of vehicle passing data on any date, and the storage module includes:
the fifth determining submodule is used for determining the vehicle passing data of the target vehicle in the current day on the date and taking the vehicle passing data in the current day as the first group of vehicle passing data on the date;
the acquisition submodule is used for acquiring pre-stored front N-1 groups of vehicle passing data of the target vehicle on the day before the date;
the calculation submodule is used for adding the group of vehicle passing data and the first group of vehicle passing data of the date to obtain N-1 groups of vehicle passing data of the date aiming at each group of vehicle passing data in the previous N-1 groups of vehicle passing data of the previous day;
and the storage submodule is used for sequentially storing the first group of vehicle passing data on the date and the N-1 groups of vehicle passing data on the date as the N groups of vehicle passing data on the date.
Optionally, in an embodiment of the present invention, the fifth determining sub-module is specifically configured to:
determining the checkpoint information of the reference area and the driving record of the target vehicle in the current day;
and determining the passing data of the target vehicle in the current day based on the checkpoint information and the driving record.
Optionally, in an embodiment of the present invention, the determining module 302 is specifically configured to:
calculating a difference between the number of the entering times and the number of the leaving times;
and judging whether the calculated time difference is larger than a first threshold value, and if so, judging that the target vehicle meets a preset abnormal license plate condition.
Optionally, in the embodiment of the present invention, the vehicle passing data further includes a total number of times in the domain; wherein the total number of times in the area is the total number of times of passing the target vehicle in the reference area;
the determining module 302 is specifically configured to:
summing the entering times and the leaving times to obtain the total entering and exiting times of the target vehicle in the reference area;
calculating the difference between the total number of the accesses and the total number of the accesses in the domain;
and judging whether the calculated time difference is larger than a second threshold value, and if so, judging that the passing data meets the preset abnormal license plate condition.
In the scheme provided by the embodiment of the invention, the times that a vehicle enters and leaves a preset reference area in a period of time are considered to be similar, and if the deviation of the two times is larger, the possibility that the corresponding vehicle is a vehicle with an abnormal license plate is higher. Therefore, based on the condition, the abnormal license plate condition can be preset, and when whether a target vehicle is a vehicle with an abnormal license plate is judged, vehicle passing data of the target vehicle in a target time period is determined firstly; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle; and then, judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate. Therefore, the purpose of effectively identifying the vehicle with the abnormal license plate can be achieved by applying the embodiment of the invention.
Corresponding to the above method embodiments, the embodiment of the present invention further provides an electronic device, as shown in fig. 4, which may include a processor 401 and a memory 402, wherein,
the memory 402 for storing a computer program;
the processor 401 is configured to implement the steps of the vehicle identification method provided by the embodiment of the present invention when executing the program stored in the memory 402.
The Memory may include a RAM (Random Access Memory) or an NVM (Non-Volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Through above-mentioned electronic equipment, can realize: considering that the times of a vehicle entering and leaving a preset reference area in a period of time should be similar, if the deviation between the two times is large, the corresponding vehicle is more likely to be a vehicle with an abnormal license plate. Therefore, based on the condition, the abnormal license plate condition can be preset, and when whether a target vehicle is a vehicle with an abnormal license plate is judged, vehicle passing data of the target vehicle in a target time period is determined firstly; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle; and then, judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate. Therefore, the purpose of effectively identifying the vehicle with the abnormal license plate can be achieved by applying the embodiment of the invention.
In addition, corresponding to the vehicle identification method provided in the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the vehicle identification method provided in the embodiment of the present invention.
The above-described computer-readable storage medium stores an application program that executes the vehicle identification method provided by the embodiment of the present invention when executed, and thus can realize: considering that the times of a vehicle entering and leaving a preset reference area in a period of time should be similar, if the deviation between the two times is large, the corresponding vehicle is more likely to be a vehicle with an abnormal license plate. Therefore, based on the condition, the abnormal license plate condition can be preset, and when whether a target vehicle is a vehicle with an abnormal license plate is judged, vehicle passing data of the target vehicle in a target time period is determined firstly; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle; and then, judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate. Therefore, the purpose of effectively identifying the vehicle with the abnormal license plate can be achieved by applying the embodiment of the invention.
For the embodiments of the electronic device and the computer-readable storage medium, since the contents of the related methods are substantially similar to those of the foregoing embodiments of the methods, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the embodiments of the methods.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (18)
1. A vehicle identification method, characterized by comprising:
determining vehicle passing data of a target vehicle in a target time period; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle;
and judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate.
2. The method of claim 1, wherein the determining the passing data for the target vehicle over the target time period comprises:
and determining the passing data of the target vehicle in the target time period from the pre-stored passing data of the target vehicle in each time period.
3. The method of claim 2, wherein the respective time periods comprise: each date field;
the storage mode of the passing data of the target vehicle in each time period comprises the following steps: storing the passing data of the target vehicle in each date section by taking each date as a storage keyword;
wherein, for each date, there are N sets of vehicle passing data, of which a first set represents vehicle passing data within a date section constituted by the current date of the target vehicle and an nth set represents vehicle passing data within a date section constituted by the current date of the target vehicle and previous N-1 days; wherein N is a natural number greater than 1.
4. The method according to claim 3, wherein the determining the passing data of the target vehicle in the target time period from the pre-stored passing data of the target vehicle in each time period comprises:
determining an end date of the target date field;
determining multiple groups of vehicle passing data with storage keywords as the termination dates from pre-stored vehicle passing data of target vehicles in each date field;
determining the number of days occupied by the target date field;
and determining a group of the passing data with the arrangement number of the passing data group being the number of days in the determined multiple groups of the passing data to obtain the passing data of the target vehicle in the target date field.
5. The method of claim 3, wherein the storing of the N sets of passing data on any one date comprises:
determining the vehicle passing data of the target vehicle in the current day of the date, and taking the vehicle passing data in the current day as a first set of vehicle passing data of the date;
acquiring pre-stored front N-1 groups of vehicle passing data of the target vehicle on the day before the date;
adding the group of vehicle passing data and the first group of vehicle passing data of the date to obtain N-1 groups of vehicle passing data of the date aiming at each group of vehicle passing data in the previous N-1 groups of vehicle passing data of the previous day;
and sequentially storing the first group of vehicle passing data on the date and the N-1 groups of vehicle passing data on the date as the N groups of vehicle passing data on the date.
6. The method of claim 5, wherein determining the passing data for the target vehicle within the current day of the date comprises:
determining the checkpoint information of the reference area and the driving record of the target vehicle in the current day;
and determining the passing data of the target vehicle in the current day based on the checkpoint information and the driving record.
7. The method of claim 1, wherein the determining whether the target vehicle meets a preset abnormal license plate condition based on the number of entering times and the number of leaving times comprises:
calculating a difference between the number of the entering times and the number of the leaving times;
and judging whether the calculated time difference is larger than a first preset threshold value or not, and if so, judging that the target vehicle meets a preset abnormal license plate condition.
8. The method of claim 1, wherein the passing data further comprises a total number of times within a domain; wherein the total number of times in the area is the total number of times of passing the target vehicle in the reference area;
the judging whether the target vehicle meets the preset abnormal license plate condition or not based on the entering times and the leaving times comprises the following steps:
summing the entering times and the leaving times to obtain the total entering and exiting times of the target vehicle in the reference area;
calculating the difference between the total number of the accesses and the total number of the accesses in the domain;
and judging whether the calculated time difference is larger than a second threshold value, and if so, judging that the passing data meets the preset abnormal license plate condition.
9. A vehicle identification device characterized by comprising:
the determining module is used for determining vehicle passing data of the target vehicle in the target time period; wherein the vehicle passing data comprises: for a preset reference area, the number of times of entry and the number of times of exit of the target vehicle;
and the judging module is used for judging whether the target vehicle meets a preset abnormal license plate condition or not based on the entering times and the leaving times, and if so, judging that the target vehicle is a vehicle with an abnormal license plate.
10. The apparatus of claim 9, wherein the determining module is specifically configured to:
and determining the passing data of the target vehicle in the target time period from the pre-stored passing data of the target vehicle in each time period.
11. The apparatus of claim 10, wherein the respective time periods comprise: each date field;
the storage mode of the passing data of the target vehicle in each time period comprises the following steps: taking each date as a storage keyword, and storing the passing data of the target vehicle in each date section by a storage module;
wherein, for each date, there are N sets of vehicle passing data, of which a first set represents vehicle passing data within a date section constituted by the current date of the target vehicle and an nth set represents vehicle passing data within a date section constituted by the current date of the target vehicle and previous N-1 days; wherein N is a natural number greater than 1.
12. The apparatus of claim 11, wherein the determining module comprises:
a first determining submodule for determining an end date of the target date field;
the second determining submodule is used for determining a plurality of groups of vehicle passing data with storage keywords as the termination dates from the vehicle passing data of the target vehicle in each date field pre-stored in the storage module;
a third determining submodule for determining the number of days occupied by the target date field;
and the fourth determining submodule is used for determining a group of the vehicle passing data with the arrangement number of the vehicle passing data group being the number of days in the determined groups of the vehicle passing data to obtain the vehicle passing data of the target vehicle in the target date field.
13. The apparatus of claim 11, wherein the storage module is configured to store N sets of passing data for any date, the storage module comprising:
the fifth determining submodule is used for determining the vehicle passing data of the target vehicle in the current day on the date and taking the vehicle passing data in the current day as the first group of vehicle passing data on the date;
the acquisition submodule is used for acquiring pre-stored front N-1 groups of vehicle passing data of the target vehicle on the day before the date;
the calculation module is used for adding the group of vehicle passing data and the first group of vehicle passing data of the date to obtain N-1 groups of vehicle passing data of the date aiming at each group of vehicle passing data in the previous N-1 groups of vehicle passing data of the previous day;
and the storage submodule is used for sequentially storing the first group of vehicle passing data on the date and the N-1 groups of vehicle passing data on the date as the N groups of vehicle passing data on the date.
14. The apparatus according to claim 13, wherein the fifth determining submodule is specifically configured to:
determining the checkpoint information of the reference area and the driving record of the target vehicle in the current day;
and determining the passing data of the target vehicle in the current day based on the checkpoint information and the driving record.
15. The apparatus of claim 9, wherein the determining module is specifically configured to:
calculating a difference between the number of the entering times and the number of the leaving times;
and judging whether the calculated time difference is larger than a first threshold value, and if so, judging that the target vehicle meets a preset abnormal license plate condition.
16. The apparatus of claim 9, wherein the passing data further comprises a total number of times within a domain; wherein the total number of times in the area is the total number of times of passing the target vehicle in the reference area;
the judgment module is specifically configured to:
summing the entering times and the leaving times to obtain the total entering and exiting times of the target vehicle in the reference area;
calculating the difference between the total number of the accesses and the total number of the accesses in the domain;
and judging whether the calculated time difference is larger than a second threshold value, and if so, judging that the passing data meets the preset abnormal license plate condition.
17. An electronic device comprising a processor and a memory, wherein,
the memory is used for storing a computer program;
the processor, when executing the program stored in the memory, implementing the method steps of any of claims 1-8.
18. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of the claims 1-8.
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Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102289659A (en) * | 2011-06-23 | 2011-12-21 | 杭州诚道科技有限公司 | Companion vehicle detection method |
| CN102426786A (en) * | 2011-11-15 | 2012-04-25 | 无锡港湾网络科技有限公司 | Intelligent video analyzing system and method for automatically identifying fake plate vehicle |
| CN103413440A (en) * | 2013-04-11 | 2013-11-27 | 江苏省邮电规划设计院有限责任公司 | Fake-licensed vehicle identification method based on smart city data base and identification rule base |
| CN103514745A (en) * | 2013-10-16 | 2014-01-15 | 南京城市智能交通有限公司 | Fake license plate vehicle recognition method based on intelligent transportation |
| CN103700262A (en) * | 2013-12-26 | 2014-04-02 | 福建省视通光电网络有限公司 | Automatic area acquisition method for fake-licensed vehicles |
| CN103914986A (en) * | 2014-03-14 | 2014-07-09 | 浙江宇视科技有限公司 | Method and device for fake-license-plate analysis |
| CN104021677A (en) * | 2014-06-25 | 2014-09-03 | 浙江宇视科技有限公司 | Method and device for detecting turnover of number plate |
| CN104050813A (en) * | 2014-06-30 | 2014-09-17 | 浙江宇视科技有限公司 | Vehicle plate turning detecting method and device |
| CN104103173A (en) * | 2014-07-22 | 2014-10-15 | 银江股份有限公司 | Searching deployment-control method for fake-licensed vehicles |
| CN105448099A (en) * | 2015-12-14 | 2016-03-30 | 南威软件股份有限公司 | Motor vehicle fake license identification method based on big data |
| CN105719489A (en) * | 2016-03-24 | 2016-06-29 | 银江股份有限公司 | Fake-licensed vehicle detection method based on bayonet vehicle flow direction probability |
| CN106710225A (en) * | 2015-07-29 | 2017-05-24 | 杭州海康威视***技术有限公司 | Identification method and monitoring platform for illegal operation related with vehicle license plate |
| CN107103761A (en) * | 2017-05-22 | 2017-08-29 | 广东艾科智泊科技股份有限公司 | A kind of fake-licensed car alarm method and system based on same parking data storehouse |
| CN107204114A (en) * | 2016-03-18 | 2017-09-26 | 中兴通讯股份有限公司 | A kind of recognition methods of vehicle abnormality behavior and device |
| CN107563288A (en) * | 2017-07-31 | 2018-01-09 | 东软集团股份有限公司 | A kind of recognition methods of fake-licensed car vehicle and device |
| CN107610466A (en) * | 2017-09-25 | 2018-01-19 | 安徽畅通行交通信息服务有限公司 | A kind of region Vehicular real time monitoring management system |
| CN107680385A (en) * | 2017-10-27 | 2018-02-09 | 泰华智慧产业集团股份有限公司 | A kind of method and system for determining fake-licensed car |
| CN107861956A (en) * | 2016-09-22 | 2018-03-30 | 杭州海康威视***技术有限公司 | A kind of bayonet socket crosses the querying method and device of car data record |
| CN107895487A (en) * | 2017-11-24 | 2018-04-10 | 泰华智慧产业集团股份有限公司 | It is a kind of that the method for similar car plate string simultaneously is carried out based on big data |
| CN107993444A (en) * | 2017-11-22 | 2018-05-04 | 紫光捷通科技股份有限公司 | The suspicion car identification of car big data analysis is crossed based on bayonet |
| CN108091140A (en) * | 2016-11-23 | 2018-05-29 | 杭州海康威视数字技术股份有限公司 | A kind of method and apparatus of definite fake license plate vehicle |
| CN108492580A (en) * | 2018-04-23 | 2018-09-04 | 泰华智慧产业集团股份有限公司 | Judge the method and system of case-involving suspected vehicles |
| WO2018176191A1 (en) * | 2017-03-27 | 2018-10-04 | 深圳中兴力维技术有限公司 | Method and apparatus for identifying vehicle with fake registration plate |
-
2018
- 2018-11-09 CN CN201811330662.XA patent/CN111179603B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102289659A (en) * | 2011-06-23 | 2011-12-21 | 杭州诚道科技有限公司 | Companion vehicle detection method |
| CN102426786A (en) * | 2011-11-15 | 2012-04-25 | 无锡港湾网络科技有限公司 | Intelligent video analyzing system and method for automatically identifying fake plate vehicle |
| CN103413440A (en) * | 2013-04-11 | 2013-11-27 | 江苏省邮电规划设计院有限责任公司 | Fake-licensed vehicle identification method based on smart city data base and identification rule base |
| CN103514745A (en) * | 2013-10-16 | 2014-01-15 | 南京城市智能交通有限公司 | Fake license plate vehicle recognition method based on intelligent transportation |
| CN103700262A (en) * | 2013-12-26 | 2014-04-02 | 福建省视通光电网络有限公司 | Automatic area acquisition method for fake-licensed vehicles |
| CN103914986A (en) * | 2014-03-14 | 2014-07-09 | 浙江宇视科技有限公司 | Method and device for fake-license-plate analysis |
| CN104021677A (en) * | 2014-06-25 | 2014-09-03 | 浙江宇视科技有限公司 | Method and device for detecting turnover of number plate |
| CN104050813A (en) * | 2014-06-30 | 2014-09-17 | 浙江宇视科技有限公司 | Vehicle plate turning detecting method and device |
| CN104103173A (en) * | 2014-07-22 | 2014-10-15 | 银江股份有限公司 | Searching deployment-control method for fake-licensed vehicles |
| CN106710225A (en) * | 2015-07-29 | 2017-05-24 | 杭州海康威视***技术有限公司 | Identification method and monitoring platform for illegal operation related with vehicle license plate |
| CN105448099A (en) * | 2015-12-14 | 2016-03-30 | 南威软件股份有限公司 | Motor vehicle fake license identification method based on big data |
| CN107204114A (en) * | 2016-03-18 | 2017-09-26 | 中兴通讯股份有限公司 | A kind of recognition methods of vehicle abnormality behavior and device |
| CN105719489A (en) * | 2016-03-24 | 2016-06-29 | 银江股份有限公司 | Fake-licensed vehicle detection method based on bayonet vehicle flow direction probability |
| CN107861956A (en) * | 2016-09-22 | 2018-03-30 | 杭州海康威视***技术有限公司 | A kind of bayonet socket crosses the querying method and device of car data record |
| CN108091140A (en) * | 2016-11-23 | 2018-05-29 | 杭州海康威视数字技术股份有限公司 | A kind of method and apparatus of definite fake license plate vehicle |
| WO2018176191A1 (en) * | 2017-03-27 | 2018-10-04 | 深圳中兴力维技术有限公司 | Method and apparatus for identifying vehicle with fake registration plate |
| CN107103761A (en) * | 2017-05-22 | 2017-08-29 | 广东艾科智泊科技股份有限公司 | A kind of fake-licensed car alarm method and system based on same parking data storehouse |
| CN107563288A (en) * | 2017-07-31 | 2018-01-09 | 东软集团股份有限公司 | A kind of recognition methods of fake-licensed car vehicle and device |
| CN107610466A (en) * | 2017-09-25 | 2018-01-19 | 安徽畅通行交通信息服务有限公司 | A kind of region Vehicular real time monitoring management system |
| CN107680385A (en) * | 2017-10-27 | 2018-02-09 | 泰华智慧产业集团股份有限公司 | A kind of method and system for determining fake-licensed car |
| CN107993444A (en) * | 2017-11-22 | 2018-05-04 | 紫光捷通科技股份有限公司 | The suspicion car identification of car big data analysis is crossed based on bayonet |
| CN107895487A (en) * | 2017-11-24 | 2018-04-10 | 泰华智慧产业集团股份有限公司 | It is a kind of that the method for similar car plate string simultaneously is carried out based on big data |
| CN108492580A (en) * | 2018-04-23 | 2018-09-04 | 泰华智慧产业集团股份有限公司 | Judge the method and system of case-involving suspected vehicles |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112885101A (en) * | 2021-03-30 | 2021-06-01 | 浙江大华技术股份有限公司 | Method and device for determining abnormal equipment, storage medium and electronic device |
| CN112885101B (en) * | 2021-03-30 | 2022-06-14 | 浙江大华技术股份有限公司 | Method and device for determining abnormal equipment, storage medium and electronic device |
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