CN112528901A - Vehicle aggregation alarm method and system based on big data - Google Patents
Vehicle aggregation alarm method and system based on big data Download PDFInfo
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Abstract
The invention relates to a vehicle aggregation warning method and a vehicle aggregation warning system based on big data, which comprises the steps of preprocessing received terminal access image data to obtain picture digital information of each piece of access image data; the terminal access image data comprises at least one piece of access image data; acquiring the capturing time of a unit time period and the unique vehicle capturing data of capturing equipment by identifying the digital picture information; and judging whether the vehicle snapshot data in the time period has the vehicle aggregation warning risk or not, and determining vehicle aggregation warning information. The scheme is based on the correlation analysis of the vehicle passing big data, so that whether the passing frequency of all vehicles meets the gathering condition or not is judged, and the warning effect is achieved.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to an intelligent information issuing system for a subway station.
Background
At present, vehicle aggregation causes frequent events such as the fact that a vehicle owner is busy pulling a banner and the like, particularly the events occur in a concentrated mode in a taxi, and therefore the vehicle aggregation behaviors need to be better mastered in time. However, since the amount of vehicle passing data per card port is too large, analysis needs to be performed based on the large vehicle passing data, and the analyzed information is used as a basis, so that the phenomenon is properly controlled and managed.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent information issuing system for a subway station, which is used for analyzing vehicle passing big data so as to judge whether the passing frequency of all taxis meets a preset aggregation alarm condition or not, thereby achieving the effect of alarm warning, realizing the timely control of vehicle aggregation behaviors and effectively improving the current situation that the vehicle aggregation causes frequent events such as a vehicle owner alarm banner and the like.
The purpose of the invention is realized by adopting the following technical scheme:
a big data based vehicle aggregation warning method, the method comprising:
preprocessing received terminal access image data to obtain picture digital information of each piece of access image data; wherein the terminal access image data comprises at least one piece of access image data;
identifying the digital picture information, and acquiring the capturing time of a unit time period and the unique vehicle capturing data of capturing equipment;
and judging whether the vehicle snapshot data in the time period has the vehicle aggregation warning risk or not, and determining vehicle aggregation warning information.
Preferably, the preprocessing the received terminal access image data includes:
screening out failure data in the access image data; wherein the failure data comprises: data which cannot be accessed by the picture and the size of which exceeds the bearing range of the server;
performing data cleaning on the access image data after the failure data is screened out;
and after field renaming and default value giving are carried out on the access image data obtained through data cleaning, the access image data is stored in a temporary storage container as cleaning data.
Further, the data cleaning of the access image data after the screening of the failure data comprises:
acquiring a content field contained in each piece of access image data, and deleting data which does not contain a key field in the content field; the key fields comprise bayonet ID, snapshot time, driving direction and latitude and longitude information of the snapshot equipment;
and deleting error data of which the difference value between the shooting time and the time synchronization server exceeds a preset time range.
Preferably, the accessing the image data includes: the vehicle-passing picture, the vehicle-passing snapshot time, the snapshot device information, the driving direction mark, the driving lane mark and the vehicle speed.
Preferably, the obtaining of the picture digital information of each piece of access image data includes:
acquiring cleaning data stored in a temporary storage container;
and pushing the cleaning data, the identification parameters associated with the cleaning data and the latest vehicle year model number to an identification program through a transfer program to perform data formatting processing, so as to generate structured picture digital information.
Further, the picture digital information comprises the license plate number of the snapshot vehicle, the color of the license plate, the model number of the vehicle, the type of the vehicle, whether the vehicle is a special vehicle, the driving direction, the driving speed and the color of the vehicle.
Preferably, the identifying the picture digital information and obtaining the vehicle snapshot data with unit time period snapshot time and unique snapshot equipment includes:
clustering picture digital information based on the checkpoint ID and the snapshot time of the snapshot device to generate a feature library;
reading data corresponding to each category feature in the feature library;
all data under each snapshot device in unit time are inquired through an interface, and after data with repeated license plate numbers are deleted, the data after duplication elimination are sorted according to the snapshot time;
and (4) keeping the last snapshot record, and obtaining the snapshot time and the vehicle snapshot data unique to the snapshot equipment.
Preferably, the determining whether the vehicle snapshot data in the time period has the vehicle aggregation warning risk includes:
acquiring snapshot time and vehicle snapshot data unique to the snapshot device;
counting the vehicle snapshot number of each driving direction under each snapshot device, and comparing the vehicle snapshot number with preset alarm threshold values of different driving directions;
when the number of the vehicles captured in any driving direction exceeds the alarm threshold value of the corresponding driving direction, sending alarm information to the handheld mobile terminal;
the alarm information comprises a gate ID of the snapshot device and the vehicle snapshot number corresponding to each driving direction, which is snapshot by the current snapshot device in a preset time.
A big data based vehicle aggregation warning system comprising:
the first acquisition module is used for preprocessing the received terminal access image data to acquire the picture digital information of each piece of access image data; wherein the terminal access image data comprises at least one piece of access image data;
the second acquisition module is used for identifying the picture digital information and acquiring the capturing time in unit time period and the unique vehicle capturing data of the capturing device;
and the alarm analysis module is used for judging whether the vehicle snapshot data in the time period has the vehicle aggregation alarm risk or not and determining the vehicle aggregation alarm information.
Preferably, the first obtaining module includes:
the screening unit is used for screening out failure data in the access image data;
the data cleaning unit is used for cleaning the data of the access image data after the failure data is screened out;
the data processing unit is used for performing field renaming and default value giving on the access image data obtained by data cleaning and storing the access image data serving as cleaning data into a temporary storage container;
the first acquisition unit is used for acquiring cleaning data stored in the temporary storage container;
the formatting processing unit is used for pushing the cleaning data, the identification parameters related to the cleaning data and the latest vehicle year model number to an identification program through a switching program to perform data formatting processing so as to generate structured picture digital information;
the second obtaining module includes:
the clustering unit is used for clustering the digital information of the pictures based on the bayonet ID and the snapshot time of the snapshot equipment to generate a feature library;
the reading unit is used for reading data corresponding to each category characteristic in the characteristic library;
the processing unit is used for inquiring all data under each snapshot device in unit time through the interface, deleting the data with repeated license plate numbers and then sequencing the data after duplication elimination according to the snapshot time;
the second acquisition unit is used for reserving the last snapshot record and acquiring the snapshot time and the vehicle snapshot data unique to the snapshot device;
the alarm analysis module comprises:
the third acquisition unit is used for acquiring the snapshot time and the vehicle snapshot data unique to the snapshot device;
the comparison unit is used for counting the vehicle snapshot number of each driving direction under each snapshot device and comparing the vehicle snapshot number with preset alarm threshold values of different driving directions;
the warning unit is used for sending warning information to the handheld mobile terminal when the number of the vehicles captured in any driving direction exceeds a warning threshold value of the corresponding driving direction; the alarm information comprises a gate ID of the snapshot device and the vehicle snapshot number corresponding to each driving direction, which is snapshot by the current snapshot device in a preset time.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a vehicle gathering alarm method and system based on big data, which can inquire and analyze the big data in a background mode, perform correlation analysis by means of vehicle passing records and equipment information of various gates, for example, analyze by combining license plates, snapshot time and longitude and latitude of various gates, and can alarm and inform the vehicle gathering condition in time, wherein the informing mode comprises color signal lamp prompt or short message informing and the like of pages, so that a large amount of manual investigation time is saved, and timeliness and accuracy of taxi gathering condition mastered by security personnel are guaranteed.
According to the vehicle aggregation alarming method and system based on the big data, analysis is carried out based on the vehicle passing big data, so that whether the passing frequency of all taxis meets the preset aggregation alarming condition or not is judged, the alarming and alarming effect is achieved, the vehicle aggregation behavior is mastered in time, and the current situation that the vehicle aggregation causes frequent events such as the alarm pulling banner of a vehicle owner is effectively improved.
Drawings
FIG. 1 is a flow chart of a big data-based vehicle aggregation warning method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a big data-based vehicle aggregation warning procedure flow process according to a third embodiment of the present invention;
fig. 3 is a schematic structural diagram of a big data-based vehicle aggregate warning system according to a fourth embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to specifically understand the technical solutions provided by the present invention, the technical solutions of the present invention will be described and illustrated in detail in the following examples. It is apparent that the embodiments provided by the present invention are not limited to the specific details familiar to those skilled in the art. The following detailed description of the preferred embodiments of the invention is intended to provide further embodiments of the invention in addition to those described herein.
Example 1:
as shown in fig. 1, a big data-based vehicle aggregation warning method provided in embodiment 1 of the present invention includes:
s1 preprocessing the received terminal access image data to obtain the picture digital information of each piece of access image data; wherein the terminal access image data comprises at least one piece of access image data; accessing image data includes: the vehicle-passing picture, the vehicle-passing snapshot time, the snapshot device information, the driving direction mark, the driving lane mark, the vehicle speed and the like.
S2, recognizing the picture digital information, and acquiring the capturing time of the unit time period and the unique vehicle capturing data of the capturing device;
and S3, judging whether the vehicle snapshot data in the time period has the vehicle aggregation alarm risk or not, and determining the vehicle aggregation alarm information.
In step S1, the preprocessing the received terminal access image data specifically includes:
screening out failure data in the access image data; wherein the failure data comprises: data which cannot be accessed by the picture and the size of which exceeds the bearing range of the server;
performing data cleaning on the access image data after the failure data is screened out;
and after field renaming and default value giving are carried out on the access image data obtained through data cleaning, the access image data is stored in a temporary storage container as cleaning data.
Further, the step of performing data cleaning on the access image data after the failure data is screened out comprises the following steps:
acquiring a content field contained in each piece of access image data, and deleting data which does not contain a key field in the content field; the key fields comprise bayonet ID, snapshot time, driving direction and latitude and longitude information of the snapshot equipment;
and deleting error data of which the difference value between the shooting time and the time synchronization server exceeds a preset time range.
In step S1, the obtaining the picture digital information of each piece of access image data includes:
acquiring cleaning data stored in a temporary storage container;
and pushing the cleaning data, the identification parameters associated with the cleaning data and the latest vehicle year model number to an identification program through a transfer program to perform data formatting processing, so as to generate structured picture digital information. The picture digital information comprises the license plate number of the snapshot vehicle, the color of the license plate, the model of the vehicle, the type of the vehicle, whether the vehicle is a special vehicle, the driving direction, the driving speed, the color of the vehicle and the like.
The received terminal access image data is sent to a program capable of receiving data by a specified mode (such as ftp, ActiveMq, Kafka subscription, redis subscription, socket authentication communication and the like) by means of picture data provided by a third party manufacturer.
After receiving, firstly, the program screens and cleans the picture information contained in the data, namely, preprocesses the received terminal access image data:
1. screening failure data such as picture inaccessibility and picture size abnormity (overlarge beyond a server bearing range or undersize) and the like;
2. screening out data with time errors (the difference between the shooting time and the time synchronization server exceeds 24 hours, and the shooting time is within 5min of the future time when the shooting time is in the future time period);
3. screening failure data without bayonet information (equipment id);
and the data is used as cleaning data to be pushed to a temporary storage container (redis queue or subscription, kafka subscription) after field renaming and default value giving are carried out on the data
Secondly, the cleaned data can be pushed to an identification program through a transfer program, and the transfer program is responsible for transmitting the cleaned data, identification parameters, a latest annual vehicle model dictionary and the like to the identification program;
the identification program identifies the transmitted data and returns identified structured data (including main data items required by the function, such as license plates and capturing equipment ids), and the program calls back the identified structured data to a storage program of the system;
and the warehousing program of the system stores the detailed vehicle data identified by the identification program into a column type storage database lightning db so as to obtain the picture digital information of each piece of access image data.
The column type storage database lightning db main fields are as follows:
info _ id (string) unique identifier
vehicle _ type _ id (Int8) vehicle identification as (vehicle tail side body identification)
Capture _ time (Int32) snapshot time
device _ id (Int64) snapshot device
direction _ id (Int8) direction of travel
speed (Int8)
model _ id (Int16) vehicle model code- > corresponding dictionary at mysql
license plate identified by license _ plate2(String)
plate _ type _ id2(Int8) license plate category code
Fields used by the geo-hash (string) Google map partitioning algorithm
color _ id (Int8) vehicle body color coding
is _ face (Int8) whether it can recognize the face of a person in a car
Whether seat _ belt _ left (Int8) is belted or not
The structured and recognized vehicle snapshot data are stored in a database through the steps, and it is known that the taxi license plates have naming rules (for example, taxi license plates in Shandong province are started by LuBT or LuUT).
In step S2, identifying the picture digital information, and obtaining the vehicle snapshot data with the unit time period snapshot time and the only snapshot device includes:
clustering picture digital information based on the checkpoint ID and the snapshot time of the snapshot device to generate a feature library;
reading data corresponding to each category feature in the feature library;
all data under each snapshot device in unit time are inquired through an interface, and after data with repeated license plate numbers are deleted, the data after duplication elimination are sorted according to the snapshot time;
and (4) keeping the last snapshot record, and obtaining the snapshot time and the vehicle snapshot data unique to the snapshot equipment.
In step S3, it is determined whether there is a risk of vehicle aggregation warning in the vehicle snapshot data in the time period, and it is determined that the vehicle aggregation warning information includes:
acquiring snapshot time and vehicle snapshot data unique to the snapshot device;
counting the vehicle snapshot number of each driving direction under each snapshot device, and comparing the vehicle snapshot number with preset alarm threshold values of different driving directions;
when the number of the vehicles captured in any driving direction exceeds the alarm threshold value of the corresponding driving direction, sending alarm information to the handheld mobile terminal;
the alarm information comprises a gate ID of the snapshot device and the vehicle snapshot number corresponding to each driving direction, which is snapshot by the current snapshot device in a preset time.
Example 2:
based on the technical principle in embodiment 1, the specific implementation manner of the present invention further provides the following embodiment for implementing the vehicle aggregation alarm method based on big data, as shown in fig. 2:
inquiring all taxi passing data of the taxi snapped by the card port within 5min through the interface, and processing the taxi passing data:
1. the snapshot devices are grouped, so that taxi data snapshot by each snapshot device is obtained, for example:
Location_1:{capture_data_11,capture_data_12,capture_data_13,...}
Location_2:{capture_data_21,capture_data_22,capture_data_23,...}
2. the license plate number is removed and taken out again, and all the license plates of the taxies are obtained (because of the accuracy of the identification program, the same license plate can have different vehicle models and years, and the license plate is regarded as a passing license plate and is the same vehicle)
Because each capture _ data contains capture time, one process needs to be carried out on the data to ensure that each license plate only keeps the last capture record (unset the capture data of the license plate in the grouping result of other capture devices), so that unique vehicle passing data grouped according to the capture devices for the capture time and the capture devices are obtained;
the embodiment can analyze and judge the data processed by grouping, for each snapshot device, there is a driving direction field (such as southeast to southwest to northwest) at the front end when the data is returned, an alarm threshold (alarm _ point) is preset in each direction, when the number of taxis snapshot by a certain snapshot device exceeds the alarm _ point, S6 alarm is performed- > page point marking red processing is performed (short message reminding needs to push organized alarm language to short message sending device), the corresponding snapshot number of each driving direction of the taxi snapshot by the checkpoint within 5min is displayed, the number of each direction is compared for warning, when a public security officer looks over the page, a list of the number of checkpoints passing through is seen first, the list shows the top 20 table labels sorted by the number of taxis reversely, and the checkpoint point marks red processing exceeding the alarm threshold of alarm _ point, when the public security personnel click the corresponding bidding red number, a taxi gathering alarm detail bullet box can be displayed on the map, and the display purpose and the realization principle of the bullet box are as follows:
the display aim is that a general survey radius (number of meters) is set to display the snapshot conditions of other equipment (called temporary associated equipment of the gate) within a square circle (number of meters) taking the point position of the gate as a center, the snapshot conditions are displayed in a bullet frame in an image-text mode, characters are the name of each temporary associated equipment and the snapshot number from early morning to present, and the snapshot picture of the taxi which is closest to the temporary associated equipment is displayed above the characters
According to the implementation principle, when providing snapshot data of a gate, a third party manufacturer provides detailed information (including but not limited to device id, driving direction capable of being snapshot, latitude and longitude information and the like) of the snapshot gate, an alarm _ detail _ meter is used for inquiring all temporary associated devices in a square circle alarm _ detail _ meter with the gate as a center, and taxi snapshot numbers and taxi details (pictures) of the gates in the period from the current morning to the current morning are inquired through an S4 interface.
Example 3:
the specific implementation manner of the invention also provides a program circulation process applied to the vehicle aggregation warning method, which mainly comprises the following steps: the method comprises the steps of snap-shooting vehicle passing picture data access of each gate, picture data formatting (identification is digital data), data storage to a data warehouse (lightning db), data interface query, alarm logic judgment and result notification.
S101, data access of snapshot vehicle passing pictures of each gate is realized, wherein a front-end camera arranged at each intersection on a street has the function of snapshot vehicle passing pictures, and the snapshot vehicle passing pictures include but are not limited to the following data
A. Picture for passing car
B. Vehicle-passing snapshot time
C. Snapshot device information
D. Driving direction mark
E. Vehicle lane marker
F. Vehicle speed
The data is accessed to a data receiving program of the system through a third party manufacturer, the data is cleaned (field format is unified) for secondary processing, and the cleaned data is pushed into a temporary storage container (redis subscription or kafka publisher queue)
S102, picture data formatting (recognizing as digital data) that the picture data is pushed to an artificial intelligence recognition program, and the system analyzes the digital information of the picture (including but not limited to the number plate number, the color of the number plate, the type of the vehicle, whether the vehicle is a special vehicle, the driving direction, the driving speed and the color of the vehicle)
S103, storing the data in a data warehouse (lightning db): saving the detailed data of the vehicle recognized by the artificial intelligence recognition program into a column type storage database lightingdb (company independent research and development database)
S104, a data query interface, namely writing an interface for querying data from a data warehouse (lightningdb) by using a language such as golang, java and the like.
S105, alarm logic judgment, namely triggering by using a php-corntab combined method or running a python script to analyze data so as to judge whether a taxi aggregation tendency exists in a period of time
S106, result notification, namely sending a short message notification to a set-top contact person for result notification by using a taxi gathering display page or short message equipment deployed in an alarm program in the system.
Example 4:
based on the same technical concept, the specific embodiment of the present invention further provides a vehicle aggregation alarm system based on big data, as shown in fig. 3, including:
the first acquisition module is used for preprocessing the received terminal access image data to acquire the picture digital information of each piece of access image data; wherein the terminal access image data comprises at least one piece of access image data;
the second acquisition module is used for identifying the picture digital information and acquiring the capturing time in unit time period and the unique vehicle capturing data of the capturing device;
and the alarm analysis module is used for judging whether the vehicle snapshot data in the time period has the vehicle aggregation alarm risk or not and determining the vehicle aggregation alarm information.
Wherein, the first obtaining module comprises:
the screening unit is used for screening out failure data in the access image data;
the data cleaning unit is used for cleaning the data of the access image data after the failure data is screened out;
the data processing unit is used for performing field renaming and default value giving on the access image data obtained by data cleaning and storing the access image data serving as cleaning data into a temporary storage container;
the first acquisition unit is used for acquiring cleaning data stored in the temporary storage container;
the formatting processing unit is used for pushing the cleaning data, the identification parameters related to the cleaning data and the latest vehicle year model number to an identification program through a switching program to perform data formatting processing so as to generate structured picture digital information;
the data scrubbing unit further includes:
the field deleting subunit is used for acquiring a content field contained in each piece of access image data and deleting data which does not contain the key field in the content field; the key fields comprise bayonet ID, snapshot time, driving direction and latitude and longitude information of the snapshot equipment;
and the error data deleting subunit is used for deleting the error data of which the difference value between the shooting time and the time synchronization server exceeds the preset time range.
The second obtaining module includes:
the clustering unit is used for clustering the digital information of the pictures based on the bayonet ID and the snapshot time of the snapshot equipment to generate a feature library;
the reading unit is used for reading data corresponding to each category characteristic in the characteristic library;
the processing unit is used for inquiring all data under each snapshot device in unit time through the interface, deleting the data with repeated license plate numbers and then sequencing the data after duplication elimination according to the snapshot time;
the second acquisition unit is used for reserving the last snapshot record and acquiring the snapshot time and the vehicle snapshot data unique to the snapshot device;
the alarm analysis module comprises:
the third acquisition unit is used for acquiring the snapshot time and the vehicle snapshot data unique to the snapshot device;
the comparison unit is used for counting the vehicle snapshot number of each driving direction under each snapshot device and comparing the vehicle snapshot number with preset alarm threshold values of different driving directions;
the warning unit is used for sending warning information to the handheld mobile terminal when the number of the vehicles captured in any driving direction exceeds a warning threshold value of the corresponding driving direction; the alarm information comprises a gate ID of the snapshot device and the vehicle snapshot number corresponding to each driving direction, which is snapshot by the current snapshot device in a preset time.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting the protection scope thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.
Claims (10)
1. A big data-based vehicle aggregation warning method is characterized by comprising the following steps:
preprocessing received terminal access image data to obtain picture digital information of each piece of access image data; wherein the terminal access image data comprises at least one piece of access image data;
identifying the digital picture information, and acquiring the capturing time of a unit time period and the unique vehicle capturing data of capturing equipment;
and judging whether the vehicle snapshot data in the time period has the vehicle aggregation warning risk or not, and determining vehicle aggregation warning information.
2. The method of claim 1, wherein preprocessing the received terminal access image data comprises:
screening out failure data in the access image data; wherein the failure data comprises: data which cannot be accessed by the picture and the size of which exceeds the bearing range of the server;
performing data cleaning on the access image data after the failure data is screened out;
and after field renaming and default value giving are carried out on the access image data obtained through data cleaning, the access image data is stored in a temporary storage container as cleaning data.
3. The method of claim 2, wherein the data cleansing of the access image data after the screening out of the failure data comprises:
acquiring a content field contained in each piece of access image data, and deleting data which does not contain a key field in the content field; the key fields comprise bayonet ID, snapshot time, driving direction and latitude and longitude information of the snapshot equipment;
and deleting error data of which the difference value between the shooting time and the time synchronization server exceeds a preset time range.
4. The method of claim 1, wherein accessing image data comprises: the vehicle-passing picture, the vehicle-passing snapshot time, the snapshot device information, the driving direction mark, the driving lane mark and the vehicle speed.
5. The method of claim 1, wherein obtaining the picture digital information for each piece of access image data comprises:
acquiring cleaning data stored in a temporary storage container;
and pushing the cleaning data, the identification parameters associated with the cleaning data and the latest vehicle year model number to an identification program through a transfer program to perform data formatting processing, so as to generate structured picture digital information.
6. The method of claim 5, wherein the picture digital information comprises a license plate number of the snapshot vehicle, a color of the license plate, a model number of the vehicle, a category of the vehicle, whether the vehicle is a special vehicle, a driving direction, a driving speed, and a color of the vehicle.
7. The method of claim 1, wherein the identifying the picture digital information and obtaining vehicle snapshot data unique to a snapshot time per unit time period and a snapshot device comprises:
clustering picture digital information based on the checkpoint ID and the snapshot time of the snapshot device to generate a feature library;
reading data corresponding to each category feature in the feature library;
all data under each snapshot device in unit time are inquired through an interface, and after data with repeated license plate numbers are deleted, the data after duplication elimination are sorted according to the snapshot time;
and (4) keeping the last snapshot record, and obtaining the snapshot time and the vehicle snapshot data unique to the snapshot equipment.
8. The method of claim 1, wherein the determining whether the vehicle snapshot data of the time period has a risk of vehicle aggregate warning comprises:
acquiring snapshot time and vehicle snapshot data unique to the snapshot device;
counting the vehicle snapshot number of each driving direction under each snapshot device, and comparing the vehicle snapshot number with preset alarm threshold values of different driving directions;
when the number of the vehicles captured in any driving direction exceeds the alarm threshold value of the corresponding driving direction, sending alarm information to the handheld mobile terminal;
the alarm information comprises a gate ID of the snapshot device and the vehicle snapshot number corresponding to each driving direction, which is snapshot by the current snapshot device in a preset time.
9. A big data based vehicle aggregation warning system, comprising:
the first acquisition module is used for preprocessing the received terminal access image data to acquire the picture digital information of each piece of access image data; wherein the terminal access image data comprises at least one piece of access image data;
the second acquisition module is used for identifying the picture digital information and acquiring the capturing time in unit time period and the unique vehicle capturing data of the capturing device;
and the alarm analysis module is used for judging whether the vehicle snapshot data in the time period has the vehicle aggregation alarm risk or not and determining the vehicle aggregation alarm information.
10. The system of claim 9, wherein the first obtaining module comprises:
the screening unit is used for screening out failure data in the access image data;
the data cleaning unit is used for cleaning the data of the access image data after the failure data is screened out;
the data processing unit is used for performing field renaming and default value giving on the access image data obtained by data cleaning and storing the access image data serving as cleaning data into a temporary storage container;
the first acquisition unit is used for acquiring cleaning data stored in the temporary storage container;
the formatting processing unit is used for pushing the cleaning data, the identification parameters related to the cleaning data and the latest vehicle year model number to an identification program through a switching program to perform data formatting processing so as to generate structured picture digital information;
the second obtaining module includes:
the clustering unit is used for clustering the digital information of the pictures based on the bayonet ID and the snapshot time of the snapshot equipment to generate a feature library;
the reading unit is used for reading data corresponding to each category characteristic in the characteristic library;
the processing unit is used for inquiring all data under each snapshot device in unit time through the interface, deleting the data with repeated license plate numbers and then sequencing the data after duplication elimination according to the snapshot time;
the second acquisition unit is used for reserving the last snapshot record and acquiring the snapshot time and the vehicle snapshot data unique to the snapshot device;
the alarm analysis module comprises:
the third acquisition unit is used for acquiring the snapshot time and the vehicle snapshot data unique to the snapshot device;
the comparison unit is used for counting the vehicle snapshot number of each driving direction under each snapshot device and comparing the vehicle snapshot number with preset alarm threshold values of different driving directions;
the warning unit is used for sending warning information to the handheld mobile terminal when the number of the vehicles captured in any driving direction exceeds a warning threshold value of the corresponding driving direction; the alarm information comprises a gate ID of the snapshot device and the vehicle snapshot number corresponding to each driving direction, which is snapshot by the current snapshot device in a preset time.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113178079A (en) * | 2021-04-06 | 2021-07-27 | 青岛以萨数据技术有限公司 | Marking system, method and storage medium for signal lamp and lane line |
| CN113421409A (en) * | 2021-06-22 | 2021-09-21 | 国网山东省电力公司鄄城县供电公司 | Monitoring method and device for power transmission line and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020174207A1 (en) * | 2001-02-28 | 2002-11-21 | Abdella Battou | Self-healing hierarchical network management system, and methods and apparatus therefor |
| CN108427718A (en) * | 2018-02-07 | 2018-08-21 | 江苏大学 | A kind of vehicle traveling information temporal index and big data analysis method |
| CN109859516A (en) * | 2019-03-13 | 2019-06-07 | 重庆皓石金科技有限公司 | A kind of taxi abnormal aggregation recognition methods and device |
| CN110766949A (en) * | 2018-07-27 | 2020-02-07 | 浙江宇视科技有限公司 | Violation snapshot method and device |
| CN111259868A (en) * | 2020-03-10 | 2020-06-09 | 北京以萨技术股份有限公司 | Convolutional neural network-based method, system and medium for detecting vehicles in reverse driving |
-
2020
- 2020-12-17 CN CN202011500826.6A patent/CN112528901A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020174207A1 (en) * | 2001-02-28 | 2002-11-21 | Abdella Battou | Self-healing hierarchical network management system, and methods and apparatus therefor |
| CN108427718A (en) * | 2018-02-07 | 2018-08-21 | 江苏大学 | A kind of vehicle traveling information temporal index and big data analysis method |
| CN110766949A (en) * | 2018-07-27 | 2020-02-07 | 浙江宇视科技有限公司 | Violation snapshot method and device |
| CN109859516A (en) * | 2019-03-13 | 2019-06-07 | 重庆皓石金科技有限公司 | A kind of taxi abnormal aggregation recognition methods and device |
| CN111259868A (en) * | 2020-03-10 | 2020-06-09 | 北京以萨技术股份有限公司 | Convolutional neural network-based method, system and medium for detecting vehicles in reverse driving |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113178079A (en) * | 2021-04-06 | 2021-07-27 | 青岛以萨数据技术有限公司 | Marking system, method and storage medium for signal lamp and lane line |
| CN113178079B (en) * | 2021-04-06 | 2022-08-23 | 青岛以萨数据技术有限公司 | Marking system, method and storage medium for signal lamp and lane line |
| CN113421409A (en) * | 2021-06-22 | 2021-09-21 | 国网山东省电力公司鄄城县供电公司 | Monitoring method and device for power transmission line and electronic equipment |
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