CN110570533B - Confusable vehicle escape system and method based on big data analysis - Google Patents
Confusable vehicle escape system and method based on big data analysis Download PDFInfo
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- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
- G07B15/063—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems using wireless information transmission between the vehicle and a fixed station
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Abstract
The invention relates to a confusable vehicle driving and escaping system and a confusable vehicle driving and escaping method based on big data analysis, wherein the system consists of a toll lane unit, a toll station unit and a road section center unit; the toll lane unit is a lane machine; the toll station unit consists of a station-level server and a station-level manager, and the station-level server is in communication connection with the station-level manager; the road section center unit is composed of a data server and an analysis server. The method adopts the system. The invention accurately identifies the confusable vehicle stealing and evading event through big data analysis, makes up for the short board of manual inspection, and can effectively strike the confusable vehicle stealing and evading phenomenon and pay arrears.
Description
Technical Field
The invention relates to a confusable vehicle driving escape system and method based on big data analysis, and belongs to the technical field of highway toll facility assistance.
Background
With the expansion of the highway network and the improvement of charging, some car owners steal highway tolls by various means under the driving of economic benefit. This behavior causes serious economic losses to the highway operating units and countries. One of the situations relates to a confusable vehicle, and the same vehicle has different vehicle type judgment at different toll station outlets, so that the confusable vehicle is the same. The confusable vehicles use the wrong judgment of the toll station, or use the forged driving license to reduce the 'approved passenger seat number' of the vehicle, or change the vehicle type privately so as to achieve the purpose of stealing and evading the fee.
At present, when distinguishing vehicle types, an expressway operating unit mainly depends on manual inspection and manual entry and audit modes, a toll collector checks vehicle models, vehicle appearances, seat layouts, vehicle nameplates and other methods, and some original inspection means such as a manually entered blacklist are combined for operation, so that the expressway operating unit has great limitation, cannot timely and comprehensively find fee-evaded vehicles, cannot effectively attack treatment, and develops a new more efficient and accurate escape analysis means aiming at the current severe situation of stealing and evading fees.
Through search and discovery, Chinese patent application with application numbers CN201711387113.1 and CN108305461A discloses a method and a device for determining a suspected vehicle with evasion fee, wherein the method comprises the following steps: acquiring first traffic data corresponding to each driving vehicle; extracting detection data in the first pass data; inputting detection data into a first suspected evasion fee vehicle determination model to obtain a first identifier or a second identifier corresponding to the detection data; the first evasion suspicion vehicle determination model is obtained by training a first training sample; the first training sample includes: the first training data of normal vehicle passing and the corresponding first identification, and the second training data of fee stealing and evading vehicle passing and the corresponding second identification; and judging whether the running vehicle is a suspected vehicle for evasion. However, in the prior art represented by the technical scheme, although various technical means for analyzing and identifying the fee evasion of the vehicles on the expressway exist, the technical scheme aiming at the easy confusion of the vehicle fee evasion is not provided.
Disclosure of Invention
The main purposes of the invention are: the problem that prior art exists is overcome, a system of easily obscuring car and fleeing for the taxi is provided based on big data analysis, and the event of easily obscuring car and fleeing for the taxi is accurately identified through big data analysis, makes up the short board of artifical inspection, can effectively strike easily obscuring car and steal for the taxi phenomenon. Meanwhile, a corresponding confusable vehicle escape method is also provided.
The technical scheme for solving the technical problems of the invention is as follows:
a confusing vehicle driving and escaping system based on big data analysis is composed of a toll lane unit, a toll station unit and a road section center unit; the toll lane is characterized in that the toll lane unit is a lane machine; the toll station unit consists of a station-level server and a station-level management machine, and the station-level server is in communication connection with the station-level management machine; the road section center unit consists of a data server and an analysis server, and the data server is in communication connection with the analysis server; the lane machine is in communication connection with a station-level server, the station-level server is in communication connection with a data server, and the analysis server is in communication connection with a station-level manager;
the analysis server is used for analyzing and obtaining the suspicion library of the confusable vehicle according to the charging data in the data server, and the station-level manager is used for auditing the suspicion library of the confusable vehicle and forming a formal library of the confusable vehicle; the lane machine is used for identifying the confusable vehicles according to the confusable vehicle formal library.
The system utilizes the charge data to carry out big data analysis, firstly analyzes the suspicion library of the easily-confused vehicle, then determines the formal library of the easily-confused vehicle through auditing, and utilizes the formal library of the easily-confused vehicle to identify the easily-confused vehicle so as to realize the phenomenon that the easily-confused vehicle steals and escapes the charge.
The technical scheme for further improving the escape system comprises the following steps:
preferably, the system has: the lane machine collects the charging data and sends the charging data to the station-level server, and the station-level server collects the charging data and sends the charging data to the charging data collection state of the data server;
the system also has: on the basis of the charging data collection state, the analysis server acquires charging data from the data server and obtains an easily-confused vehicle suspicion library through analysis, the analysis server issues the easily-confused vehicle suspicion library to a station-level management machine, the station-level management machine forms an easily-confused vehicle formal library through checking the easily-confused vehicle suspicion library and issues the easily-confused vehicle formal library to the station-level server, and the station-level server issues the easily-confused vehicle formal library to an easily-confused vehicle information issuing state of a lane machine;
the system also has: on the basis of the sending state of the confusable vehicle information, the lane machine compares the license plate of the vehicle in the current lane with the formal database of the confusable vehicle, when the comparison result is in comparison, the lane machine displays reminding information to a toll collector, and the lane machine records the event in the comparison and uploads the event to the recognition state of the confusable vehicle of the station-level server.
More preferably, the system further has: on the basis of the information issuing state of the confusable vehicles, the station-level server analyzes and counts the passage payment condition of each confusable vehicle in the confusable vehicle formal library according to the confusable vehicle formal library and the charging data, and obtains the escape following statistical state of the evasion information data of each confusable vehicle;
the system also has: on the basis of the pursuit statistical state, when the pursuit of the easy-confusion vehicle is successful, the station-level server records the pursuit successful event of the easy-confusion vehicle evasion fee, and marks and seals the evasion fee information data of the easy-confusion vehicle in the pursuit recording state.
More preferably, the lane machine is provided with a charging data acquisition module and a confusable vehicle processing module, wherein the charging data acquisition module is used for acquiring charging data, the confusable vehicle processing module is used for receiving a confusable vehicle formal library sent by the station-level server, comparing a license plate of a vehicle in the current lane with the confusable vehicle formal library, and displaying reminding information to a toll collector and recording an event in the comparison when the comparison result is in the comparison;
the station-level server is provided with a charging data summarizing module, an easily-confused vehicle formal inventory storage module, an easily-confused vehicle rate medium-rate event processing module and an easily-confused vehicle stealing and evading fee processing module, wherein, the charging data summarization module is used for summarizing charging data sent by a lane machine, the confusion vehicle formal library storage module is used for storing the confusion vehicle formal library sent by a station level management machine, the confusable vehicle-to-vehicle ratio event processing module is used for receiving a ratio event sent by a lane engine and sending out reminding information, the confusing vehicle evasion fee processing module is used for analyzing and counting the passing fee paying condition of each confusing vehicle in the confusing vehicle formal library according to the confusing vehicle formal library and the charging data, obtaining evasion fee information data of each confusing vehicle, recording an event of successful evasion fee pursuit payment of the confusing vehicle when the confusing vehicle evasion fee is successfully pursued, and marking and sealing the evasion fee information data of the confusing vehicle;
the station-level manager is provided with a suspected confusable vehicle auditing module for auditing a confusable vehicle suspicion library sent by the analysis server and forming a confusable vehicle formal library;
the data server is provided with a charging data storage module used for storing charging data sent by the station-level server;
the analysis server is provided with a suspected confusable vehicle analysis module and is used for acquiring the charging data from the data server and analyzing the charging data to obtain a confusable vehicle suspicion library.
More preferably, the suspected confusable vehicle auditing module comprises an automatic auditing submodule and a manual auditing submodule, the automatic auditing submodule is used for selecting confusable vehicles from the confusable vehicle suspicion library according to a preset automatic auditing strategy and placing the confusable vehicles into a confusable vehicle formal library, and the manual auditing submodule is used for selecting suspected confusable vehicles needing manual auditing from the confusable vehicle suspicion library according to a preset manual auditing strategy to be audited and placing the confusable vehicles determined through manual auditing into the confusable vehicle formal library.
After the preferred scheme is adopted, the easily confused vehicle stealing and fee evading event can be better identified, and the easily confused vehicle stealing and fee evading phenomenon can be better struck.
The present invention also provides:
a confusable vehicle escape method based on big data analysis is characterized in that the confusable vehicle escape system is adopted; the method comprises the following steps:
firstly, a lane machine collects charging data and sends the charging data to a station-level server, and the station-level server collects the charging data and sends the charging data to a data server;
secondly, the analysis server acquires the charging data from the data server and analyzes the charging data to obtain an easily-confused vehicle suspicion library, the analysis server issues the easily-confused vehicle suspicion library to a station-level management machine, the station-level management machine forms an easily-confused vehicle formal library through examining the easily-confused vehicle suspicion library and issues the easily-confused vehicle formal library to a station-level server, and the station-level server issues the easily-confused vehicle formal library to a lane machine;
and thirdly, comparing the license plate of the vehicle in the current lane with the formal database of the easily confused vehicle by the lane machine, displaying reminding information to a toll collector by the lane machine when the comparison result is in the comparison, and recording the event in the comparison by the lane machine and uploading the event to the station-level server.
The method utilizes the charging data to carry out big data analysis, firstly analyzes the suspicion library of the confusable vehicle, then determines the formal library of the confusable vehicle through auditing, and utilizes the formal library of the confusable vehicle to identify the confusable vehicle so as to realize the phenomenon of stealing and escaping the fee of the confusable vehicle.
The escape method of the invention has the further improved technical scheme as follows:
preferably, the method further comprises a pursuit sub-method, the pursuit sub-method comprising:
the station-level server analyzes and counts the toll payment condition of each confusable vehicle in the confusable vehicle formal library according to the confusable vehicle formal library and the charge data, and obtains the stealing and escaping charge information data of each confusable vehicle; when successful recollection of the evasion of the confusable vehicle, the station-level server records the evasion fee recollection successful event of the confusable vehicle, and marks and seals the evasion fee information data of the confusable vehicle.
Preferably, in the second step, the analysis server adopts a preset analysis strategy to analyze and obtain a suspicion library of the easily-confused vehicle;
the station-level manager adopts a preset auditing sub method to audit the suspicion library of the confusable vehicle, wherein the auditing sub method comprises the following steps:
s1, selecting the confusing vehicle from the confusing vehicle suspicion library by the station-level management machine according to a preset automatic auditing strategy and placing the confusing vehicle into a confusing vehicle formal library;
and S2, selecting the suspected confusing vehicle needing manual examination from the confusing vehicle suspicion library by the station-level management machine according to a preset manual examination strategy for examination and placing the confusing vehicle determined by manual examination into a confusing vehicle formal library.
More preferably, the analysis strategy comprises: selecting charging data in a preset time period, finding out vehicles with the passing times of more than or equal to M, and finding out vehicles with the vehicle type of more than or equal to N from the vehicles, wherein the vehicles are suspected confusable vehicles;
the automatic audit strategy comprises the following steps: respectively and sequentially carrying out 'passenger-passenger two automatic analysis', 'passenger-two three automatic analysis' and 'passenger-passenger three automatic analysis' on each vehicle in the suspicion library of the confusable vehicle, and then identifying the highest vehicle type in the analysis results as the actual vehicle type of the vehicle;
the specific process of 'automatic analysis of two customers by one customer' is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to m1, and if so, listing the second passenger in an analysis result;
the specific process of 'automatic analysis of two guests and three guests' is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m2, and if so, listing the third passenger in an analysis result;
the specific process of 'automatic analysis by one guest and three guests' is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m3, and if so, listing the third passenger in an analysis result;
the manual auditing strategy comprises the following steps: respectively and sequentially carrying out 'guest-two suspicion analysis', 'guest-two suspicion analysis' and 'guest-three suspicion analysis' on each vehicle in the suspicion library of the easily-confused vehicles, and then identifying the highest vehicle type in the analysis results as the suspect vehicle type of the vehicle;
the specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to n1, and if so, listing the second passenger in an analysis result;
the specific process of suspicion analysis of two guests and three guests is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n2, and if so, listing the third passenger in an analysis result;
the specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n3, and if so, listing the third passenger in an analysis result;
m, N, M1, M2, M3, N1, N2 and N3 are natural numbers respectively, M is more than 5, N is more than 1, M1 is more than 1, M2 is more than 1, M3 is more than 1, 1 is more than or equal to N1 and is less than M1, 1 is more than or equal to N2 and is less than or equal to M2, and 1 is more than or equal to N3 and is less than or equal to M3.
More preferably, M is 10, N is 2, M1 is M2 is 3, M3 is 2, and N1 is N2 is N3 is 1.
After the preferred scheme is adopted, the easily confused vehicle stealing and fee evading event can be better identified, and the easily confused vehicle stealing and fee evading phenomenon can be better struck.
The invention accurately identifies the confusable vehicle stealing and evading event through big data analysis, makes up for the short board of manual inspection, and can effectively strike the confusable vehicle stealing and evading phenomenon and pay arrears.
Drawings
Fig. 1 is a schematic system diagram according to embodiment 1 of the present invention.
Fig. 2 to 7 are interface diagrams of an example of implementation of embodiment 2 of the present invention.
Fig. 8 to 9 are schematic diagrams of experimental results of embodiment 2 of the present invention.
Detailed Description
The invention is described in further detail below with reference to embodiments and with reference to the drawings. The invention is not limited to the examples given.
Example 1
As shown in fig. 1, the confusable car driving escape system based on big data analysis of the present embodiment is composed of a toll lane unit, a toll station unit and a road section center unit; the toll lane unit is a lane machine; the toll station unit consists of a station-level server and a station-level manager, and the station-level server is in communication connection with the station-level manager; the road section center unit consists of a data server and an analysis server, and the data server is in communication connection with the analysis server; the lane machine is in communication connection with the station-level server, the station-level server is in communication connection with the data server, and the analysis server is in communication connection with the station-level manager; the analysis server is used for analyzing and obtaining the suspicion library of the easily-confused vehicle according to the charging data in the data server, and the station-level management machine is used for auditing the suspicion library of the easily-confused vehicle and forming a formal library of the easily-confused vehicle; and the lane machine identifies the confusable vehicle according to the confusable vehicle formal library.
Specifically, the lane machine is provided with a charging data acquisition module and an easily-confused vehicle processing module, wherein the charging data acquisition module is used for acquiring charging data, the easily-confused vehicle processing module is used for receiving an easily-confused vehicle formal library sent by a station-level server, comparing a license plate of a vehicle in a current lane with the easily-confused vehicle formal library, and displaying reminding information to a toll collector and simultaneously recording an event in the comparison when the comparison result is in comparison.
The station-level server is provided with a charging data summarizing module, an easily-confused vehicle formal inventory storage module, an easily-confused vehicle rate medium-term event processing module and an easily-confused vehicle stealing and evading fee processing module, the system comprises a lane machine, a lane confusion rate event processing module, a lane confusion rate vehicle stolen and evasion processing module, a charging data collecting module, a lane confusion rate vehicle official database storage module, a lane confusion rate event processing module and an evasion information recording module, wherein the charging data collecting module is used for collecting charging data sent by the lane machine, the confusion rate vehicle official database storage module is used for storing a confusion rate vehicle official database sent by a station level management machine, the confusion rate event processing module is used for receiving a proportion event sent by the lane machine and sending reminding information, the confusion rate vehicle stolen and evasion processing module is used for analyzing and counting the passing payment condition of each confusion rate vehicle in the confusion rate vehicle official database according to the confusion rate vehicle official database and the charging data, obtaining evasion information data of each confusion rate vehicle, recording the confusion rate vehicle stolen and evasion successful event when the confusion rate vehicle is successfully paid, and marking and sealing the confusion rate information data of the confusion rate vehicle.
The station-level manager is provided with a suspected confusable vehicle auditing module for auditing the suspected confusable vehicle database sent by the analysis server and forming a formal confusable vehicle database.
The data server has a charging data storage module for storing the charging data sent by the station-level server.
The analysis server is provided with a suspected confusable vehicle analysis module and is used for acquiring the charging data from the data server and obtaining a confusable vehicle suspicion library through analysis.
The suspected confusable vehicle auditing module comprises an automatic auditing submodule and a manual auditing submodule, the automatic auditing submodule is used for selecting confusable vehicles from the confusable vehicle suspicion library according to a preset automatic auditing strategy and placing the confusable vehicles into a confusable vehicle formal library, and the manual auditing submodule is used for selecting suspected confusable vehicles needing manual auditing from the confusable vehicle suspicion library according to a preset manual auditing strategy for auditing and placing the confusable vehicles determined through manual auditing into the confusable vehicle formal library.
The system of the embodiment has the following specific states:
(1) the lane machine collects the charging data and sends the charging data to the station-level server, and the station-level server collects the charging data and sends the charging data to the charging data collection state of the data server.
(2) On the basis of the charging data collection state, the analysis server obtains charging data from the data server and obtains an easily-confused vehicle suspicion library through analysis, the analysis server issues the easily-confused vehicle suspicion library to the station-level management machine, the station-level management machine forms an easily-confused vehicle formal library through checking the easily-confused vehicle suspicion library and sends the easily-confused vehicle formal library to the station-level server, and the station-level server issues the easily-confused vehicle formal library to the easily-confused vehicle information issuing state of the lane machine.
(3) On the basis of the sending state of the confusable vehicle information, the lane machine compares the license plate of the vehicle in the current lane with the formal database of the confusable vehicle, when the comparison result is in comparison, the lane machine displays reminding information to a toll collector, and the lane machine records the event in the comparison and uploads the event to the recognition state of the confusable vehicle of the station-level server.
(4) On the basis of the information issuing state of the confusable vehicles, the station-level server analyzes and counts the passage payment condition of each confusable vehicle in the confusable vehicle formal library according to the confusable vehicle formal library and the charging data, and obtains the evasion statistical state of the evasion information data of each confusable vehicle.
(5) On the basis of the pursuit statistical state, when the pursuit of the easy-confusion vehicle is successful, the station-level server records the pursuit successful event of the easy-confusion vehicle evasion fee, and marks and seals the evasion fee information data of the easy-confusion vehicle in the pursuit recording state.
Example 2
The confusing vehicle escape method based on big data analysis in the embodiment adopts the confusing vehicle escape system in the embodiment 1, and the method in the embodiment includes the following steps:
firstly, a lane machine collects charging data and sends the charging data to a station-level server, and the station-level server collects the charging data and sends the charging data to a data server;
secondly, the analysis server acquires the charging data from the data server and analyzes the charging data to obtain an easily-confused vehicle suspicion library, the analysis server issues the easily-confused vehicle suspicion library to a station-level management machine, the station-level management machine forms an easily-confused vehicle formal library through examining the easily-confused vehicle suspicion library and issues the easily-confused vehicle formal library to a station-level server, and the station-level server issues the easily-confused vehicle formal library to a lane machine;
and thirdly, comparing the license plate of the vehicle in the current lane with the formal database of the easily confused vehicle by the lane machine, displaying reminding information to a toll collector by the lane machine when the comparison result is in the comparison, and recording the event in the comparison by the lane machine and uploading the event to the station-level server.
Specifically, in the second step, the analysis server adopts a preset analysis strategy to analyze and obtain the suspicion library of the confusable vehicle.
The station-level manager adopts a preset auditing sub method to audit the suspicion library of the confusable vehicle, and the auditing sub method comprises the following steps:
and S1, selecting the confusing vehicle from the confusing vehicle suspicion library by the station-level management machine according to a preset automatic auditing strategy, and placing the confusing vehicle into a confusing vehicle formal library.
And S2, selecting the suspected confusing vehicle needing manual examination from the confusing vehicle suspicion library by the station-level management machine according to a preset manual examination strategy for examination and placing the confusing vehicle determined by manual examination into a confusing vehicle formal library.
The analysis strategy comprises the following steps: and selecting the charging data in a preset time period, finding out vehicles with the passing times of more than or equal to M, and finding out vehicles with the vehicle type of more than or equal to N from the vehicles, wherein the vehicles are suspected confusable vehicles.
The automatic auditing strategy comprises the following steps: and respectively and sequentially carrying out 'passenger-two automatic analysis', 'passenger-two-passenger-three automatic analysis' and 'passenger-three automatic analysis' on each vehicle in the suspicion library of the confusable vehicle, and then identifying the highest vehicle type in the analysis results as the actual vehicle type of the vehicle.
The specific process of 'automatic analysis by guest and guest two' is as follows: firstly, judging whether the vehicle type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to m1, and if so, listing the second passenger in the analysis result.
The specific process of the automatic analysis of two guests and three guests is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m2, and if so, listing the third passenger in the analysis result.
The specific process of 'automatic analysis by one guest and three guests' is as follows: firstly, judging whether the current vehicle type of the vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m3, and if so, listing the third passenger in the analysis result.
The manual auditing strategy comprises the following steps: and respectively carrying out 'guest-two suspicion analysis', 'guest-two suspicion analysis' and 'guest-three suspicion analysis' on each vehicle in the suspicion library of the easily-confused vehicle, and then identifying the highest vehicle type in the analysis result as the suspected vehicle type of the vehicle.
The specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the vehicle type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to n1, and if so, listing the second passenger in the analysis result.
The specific process of suspicion analysis of guest, guest and guest is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n2, and if so, listing the third passenger in the analysis result.
The concrete process of' suspicion analysis of guest and guest is as follows: firstly, judging whether the current vehicle type of the vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n3, and if so, listing the third passenger in the analysis result.
M, N, M1, M2, M3, N1, N2 and N3 are natural numbers respectively, M is more than 5, N is more than 1, M1 is more than 1, M2 is more than 1, M3 is more than 1, 1 is more than or equal to N1 and is less than M1, 1 is more than or equal to N2 and is less than or equal to M2, and 1 is more than or equal to N3 and is less than or equal to M3. In this embodiment, M is 10, N is 2, M1 is M2 is 3, M3 is 2, and N1 is N2 is N3 is 1.
In addition, the method of this embodiment further includes a catch-up sub-method, which includes:
the station-level server analyzes and counts the toll payment condition of each confusable vehicle in the confusable vehicle formal library according to the confusable vehicle formal library and the charge data, and obtains the stealing and escaping charge information data of each confusable vehicle; when successful recollection of the evasion of the confusable vehicle, the station-level server records the evasion fee recollection successful event of the confusable vehicle, and marks and seals the evasion fee information data of the confusable vehicle.
An implementation example of this embodiment is as follows:
firstly, setting specific functions of a toll lane unit, a toll station unit and a road section center unit.
1. A toll lane unit:
(1) data update
And starting an updating thread when the program is started by the lane machine, and automatically sending the program to the station level server once in 5 minutes to detect whether a new version confusing vehicle formal library is updated. If yes, the latest confusion vehicle formal library is automatically downloaded to the local and loaded into a program memory for standby.
(2) Confusing vehicle identification and result uploading
The lane machine compares the license plate of the vehicle in the current lane with the formal database of the easily-confused vehicle, when the comparison result is in comparison, the lane machine displays reminding information to a toll collector to avoid the toll collector from entering errors, records the events in the comparison and uploads the events to the station-level server for background statistical analysis, and the toll station can also process the vehicle according to the events.
2. Toll station unit
(1) User login
As shown in FIG. 2, the user inputs the job number and the password, and logs in the software after the check is successful.
(2) Easily confused vehicle audit
A. Automatic auditing
And selecting the confusable vehicle from the confusable vehicle suspicion library according to a preset automatic auditing strategy and placing the confusable vehicle into a confusable vehicle formal library.
B. Manual review
As shown in fig. 3, a suspected confusing vehicle needing manual review is selected from the confusing vehicle suspicion library according to a preset manual review policy, is displayed to an exclusive reviewer of the toll station, is manually judged and reviewed for 2 times through elements such as vehicle pictures and the like, and is placed into the confusing vehicle formal library after being manually reviewed and determined.
(3) Formal vehicle type library
As shown in fig. 4, each station maintains a formal warehouse of confusable vehicles used by the station, and the warehouse has correct vehicle type data of confusable vehicles which pass through the station at high frequency, so that the toll station can check and manage the vehicle type data.
(4) Escape pursuit result
As shown in fig. 5, the toll payment condition of each confusable vehicle in the confusable vehicle master library is analyzed and counted according to the confusable vehicle master library and the charging data, and if the correct vehicle type is higher than the past historical passing vehicle type, the stolen and escaped fee is counted. And the station-level server automatically extracts the passing record of the vehicle at the station in the past 2 years, checks the passing charge amount which is calculated by the correct vehicle type and is to be received, and counts the fee evasion amount caused by the vehicle type difference of the vehicle in the past 2 years.
The toll station can print all the passing records, the passing pictures and the actually receivable amount of the suspected fee evasion when the fee evasion amount is chased for the vehicle for the evidence of chasing.
After the pursuit payment is completed, the station-level server records the evasion fee pursuit payment success event of the confusable vehicle, and marks and seals evasion fee information data of the confusable vehicle.
(5) Statistical analysis of
The recognition results of the analysis strategy and the accompanying escape results can be displayed in the form of charts and reports.
(6) And checking and recording
As shown in fig. 6, the station-level server may record the auditing time of the automatic audit and the manual audit of the confusable vehicle, the name of the auditor and the policy used, the number of audited vehicles, and other information.
(7) Policy configuration
The configuration interface is shown in fig. 7.
A. And automatically checking the policy configuration, specifically adopting the corresponding configuration of the embodiment.
B. And manually checking the policy configuration, specifically adopting the corresponding configuration of the embodiment.
3. Road section center unit
(1) And collecting charging data, wherein the function is shown in corresponding content of the embodiment.
(2) Suspect car data analysis
The analysis server analyzes the passing records of the past year every morning, obtains the suspicion library of the easily-confused vehicle according to the analysis strategy analysis corresponding to the embodiment, and pushes the suspicion library of the easily-confused vehicle to the toll station unit with higher passing frequency.
(3) Data pushing of suspicion vehicle
And automatically pushing the suspicion library of the confusable vehicle to a specified toll station unit every day.
The following results can be obtained by internal tests in the embodiment:
1. the recording and auditing efficiency of the confusable vehicle is high
By 12 months end in 2018, 2715 confusable vehicles recorded in a central confusable system by a toll station in the past year by adopting the existing method are accumulated. Starting in 11 middle of the month, 4315 total results of the test according to this example were obtained. The comparison between the two results shows that the input auditing efficiency is greatly improved. As shown in fig. 8, the left side is the conventional method, and the right side is the present embodiment.
2. The passing coverage rate of the easily confused vehicles is high
The data of the miscible library of the central system in the past year is compared with the test results of the embodiment. It is found that the passing flow rate of the miscible vehicles at a certain toll station in the central miscible warehouse is 49.95 ten thousand times, 4315 miscible vehicles are analyzed through the test of the embodiment, the analyzed vehicles are recorded into the database for analysis and comparison, the result shows that the passing flow rate at a certain toll station is up to 16.77 ten thousand times, and the flow rate ratio analyzed by the central miscible warehouse and the embodiment is 1: 11, the ratio of the amount is 1: 9.64. it is further proved that the vehicle easy to be confused through the strategy analysis of the embodiment has higher traffic rate at a certain toll station, reduces the consumption of lane hard disks and memory resources, and improves the comparison efficiency and the working performance of the vehicle easy to be confused on the lane.
3. The embodiment has accurate and efficient escape
According to statistics, the confusing vehicle analyzed and found in the embodiment passes by 3.71 ten thousand times with a low vehicle type in the huning section in 2018, and has a loss toll of 42.57 ten thousand. Wherein the passing times of low vehicle types at a certain section of the Shanhuning road are 3.45 ten thousand times, accounting for 93 percent; the loss toll is 35.64 ten thousand, and accounts for 83.7 percent. Therefore, the preference fee evasion paths of the confusable vehicles obtained by analysis are concentrated on a certain road section, so that the method is more accurate and efficient in the process of checking and evading the road section.
In addition to the above embodiments, the present invention may have other embodiments. All technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (7)
1. A confusing vehicle driving and escaping system based on big data analysis is composed of a toll lane unit, a toll station unit and a road section center unit; the toll lane is characterized in that the toll lane unit is a lane machine; the toll station unit consists of a station-level server and a station-level management machine, and the station-level server is in communication connection with the station-level management machine; the road section center unit consists of a data server and an analysis server, and the data server is in communication connection with the analysis server; the lane machine is in communication connection with a station-level server, the station-level server is in communication connection with a data server, and the analysis server is in communication connection with a station-level manager;
the analysis server is used for analyzing and obtaining the suspicion library of the confusable vehicle according to the charging data in the data server, and the station-level manager is used for auditing the suspicion library of the confusable vehicle and forming a formal library of the confusable vehicle; the lane machine is used for identifying the confusable vehicles according to the confusable vehicle formal library;
the system has: the lane machine collects the charging data and sends the charging data to the station-level server, and the station-level server collects the charging data and sends the charging data to the charging data collection state of the data server;
the system also has: on the basis of the charging data collection state, the analysis server acquires charging data from the data server and obtains an easily-confused vehicle suspicion library through analysis, the analysis server issues the easily-confused vehicle suspicion library to a station-level management machine, the station-level management machine forms an easily-confused vehicle formal library through checking the easily-confused vehicle suspicion library and issues the easily-confused vehicle formal library to the station-level server, and the station-level server issues the easily-confused vehicle formal library to an easily-confused vehicle information issuing state of a lane machine;
the system also has: on the basis of the sending state of the confusable vehicle information, the lane machine compares the license plate of the vehicle in the current lane with the formal database of the confusable vehicle, when the comparison result is in comparison, the lane machine displays reminding information to a toll collector, and the lane machine records the event in the comparison and uploads the event to the confusable vehicle identification state of the station-level server;
the analysis server analyzes and obtains a suspicion library of the easily-confused vehicle by adopting a preset analysis strategy; the analysis strategy comprises the following steps: selecting charging data in a preset time period, finding out vehicles with the passing times of more than or equal to M, and finding out vehicles with the vehicle type of more than or equal to N from the vehicles, wherein the vehicles are suspected confusable vehicles;
the station-level manager adopts a preset auditing sub method to audit the suspicion library of the confusable vehicle, and the auditing sub method comprises the following steps:
s1, selecting the confusing vehicle from the confusing vehicle suspicion library by the station-level management machine according to a preset automatic auditing strategy and placing the confusing vehicle into a confusing vehicle formal library;
s2, selecting a suspected confusing vehicle needing manual examination from the confusing vehicle suspicion library by the station-level management machine according to a preset manual examination strategy for examination, and placing the confusing vehicle determined through manual examination into a confusing vehicle formal library;
the automatic audit strategy comprises the following steps: respectively and sequentially carrying out 'passenger-passenger two automatic analysis', 'passenger-two three automatic analysis' and 'passenger-passenger three automatic analysis' on each vehicle in the suspicion library of the confusable vehicle, and then identifying the highest vehicle type in the analysis results as the actual vehicle type of the vehicle;
the specific process of 'automatic analysis of two customers by one customer' is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to m1, and if so, listing the second passenger in an analysis result;
the specific process of 'automatic analysis of two guests and three guests' is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m2, and if so, listing the third passenger in an analysis result;
the specific process of 'automatic analysis by one guest and three guests' is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m3, and if so, listing the third passenger in an analysis result;
the manual auditing strategy comprises the following steps: respectively and sequentially carrying out 'guest-two suspicion analysis', 'guest-two suspicion analysis' and 'guest-three suspicion analysis' on each vehicle in the suspicion library of the easily-confused vehicles, and then identifying the highest vehicle type in the analysis results as the suspect vehicle type of the vehicle;
the specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to n1, and if so, listing the second passenger in an analysis result;
the specific process of suspicion analysis of two guests and three guests is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n2, and if so, listing the third passenger in an analysis result;
the specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n3, and if so, listing the third passenger in an analysis result;
m, N, M1, M2, M3, N1, N2 and N3 are natural numbers respectively, M is more than 5, N is more than 1, M1 is more than 1, M2 is more than 1, M3 is more than 1, 1 is more than or equal to N1 and is less than M1, 1 is more than or equal to N2 and is less than or equal to M2, and 1 is more than or equal to N3 and is less than or equal to M3.
2. The confusing vehicle flight escape system as set forth in claim 1, further comprising: on the basis of the information issuing state of the confusable vehicles, the station-level server analyzes and counts the passage payment condition of each confusable vehicle in the confusable vehicle formal library according to the confusable vehicle formal library and the charging data, and obtains the escape following statistical state of the evasion information data of each confusable vehicle;
the system also has: on the basis of the pursuit statistical state, when the pursuit of the easy-confusion vehicle is successful, the station-level server records the pursuit successful event of the easy-confusion vehicle evasion fee, and marks and seals the evasion fee information data of the easy-confusion vehicle in the pursuit recording state.
3. The confusable vehicle driving escape system according to claim 1 or 2, wherein the lane machine is provided with a charging data acquisition module and a confusable vehicle processing module, wherein the charging data acquisition module is used for acquiring charging data, the confusable vehicle processing module is used for receiving a confusable vehicle formal library sent by a station-level server, comparing a license plate of a vehicle in a current lane with the confusable vehicle formal library, and displaying a reminding message to a toll collector when a comparison result is positive while recording an event in the comparison;
the station-level server is provided with a charging data summarizing module, an easily-confused vehicle formal inventory storage module, an easily-confused vehicle rate medium-rate event processing module and an easily-confused vehicle stealing and evading fee processing module, wherein, the charging data summarization module is used for summarizing charging data sent by a lane machine, the confusion vehicle formal library storage module is used for storing the confusion vehicle formal library sent by a station level management machine, the confusable vehicle-to-vehicle ratio event processing module is used for receiving a ratio event sent by a lane engine and sending out reminding information, the confusing vehicle evasion fee processing module is used for analyzing and counting the passing fee paying condition of each confusing vehicle in the confusing vehicle formal library according to the confusing vehicle formal library and the charging data, obtaining evasion fee information data of each confusing vehicle, recording an event of successful evasion fee pursuit payment of the confusing vehicle when the confusing vehicle evasion fee is successfully pursued, and marking and sealing the evasion fee information data of the confusing vehicle;
the station-level manager is provided with a suspected confusable vehicle auditing module for auditing a confusable vehicle suspicion library sent by the analysis server and forming a confusable vehicle formal library;
the data server is provided with a charging data storage module used for storing charging data sent by the station-level server;
the analysis server is provided with a suspected confusable vehicle analysis module and is used for acquiring the charging data from the data server and analyzing the charging data to obtain a confusable vehicle suspicion library.
4. The confusing vehicle escape system according to claim 3, wherein the suspected confusing vehicle audit module comprises an automatic audit sub-module and a manual audit sub-module, the automatic audit sub-module is configured to select a confusing vehicle from the confusing vehicle suspicion library according to a preset automatic audit policy and place the selected confusing vehicle into the confusing vehicle formal library, and the manual audit sub-module is configured to select a suspected confusing vehicle requiring manual audit from the confusing vehicle suspicion library according to a preset manual audit policy for audit and place the confusing vehicle determined through manual audit into the confusing vehicle formal library.
5. A confusable vehicle escape method based on big data analysis, which is characterized in that the confusable vehicle escape system of any one of claims 1 to 4 is adopted; the method comprises the following steps:
firstly, a lane machine collects charging data and sends the charging data to a station-level server, and the station-level server collects the charging data and sends the charging data to a data server;
secondly, the analysis server acquires the charging data from the data server and analyzes the charging data to obtain an easily-confused vehicle suspicion library, the analysis server issues the easily-confused vehicle suspicion library to a station-level management machine, the station-level management machine forms an easily-confused vehicle formal library through examining the easily-confused vehicle suspicion library and issues the easily-confused vehicle formal library to a station-level server, and the station-level server issues the easily-confused vehicle formal library to a lane machine;
thirdly, the lane machine compares the license plate of the vehicle in the current lane with the formal database of the easily confused vehicle, when the comparison result is in the comparison, the lane machine displays reminding information to a toll collector, and the lane machine records the event in the comparison and uploads the event to the station-level server;
in the second step, the analysis server adopts a preset analysis strategy to analyze and obtain a suspicion library of the easily-confused vehicle; the analysis strategy comprises the following steps: selecting charging data in a preset time period, finding out vehicles with the passing times of more than or equal to M, and finding out vehicles with the vehicle type of more than or equal to N from the vehicles, wherein the vehicles are suspected confusable vehicles;
the station-level manager adopts a preset auditing sub method to audit the suspicion library of the confusable vehicle, and the auditing sub method comprises the following steps:
s1, selecting the confusing vehicle from the confusing vehicle suspicion library by the station-level management machine according to a preset automatic auditing strategy and placing the confusing vehicle into a confusing vehicle formal library;
s2, selecting a suspected confusing vehicle needing manual examination from the confusing vehicle suspicion library by the station-level management machine according to a preset manual examination strategy for examination, and placing the confusing vehicle determined through manual examination into a confusing vehicle formal library;
the automatic audit strategy comprises the following steps: respectively and sequentially carrying out 'passenger-passenger two automatic analysis', 'passenger-two three automatic analysis' and 'passenger-passenger three automatic analysis' on each vehicle in the suspicion library of the confusable vehicle, and then identifying the highest vehicle type in the analysis results as the actual vehicle type of the vehicle;
the specific process of 'automatic analysis of two customers by one customer' is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to m1, and if so, listing the second passenger in an analysis result;
the specific process of 'automatic analysis of two guests and three guests' is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m2, and if so, listing the third passenger in an analysis result;
the specific process of 'automatic analysis by one guest and three guests' is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to m3, and if so, listing the third passenger in an analysis result;
the manual auditing strategy comprises the following steps: respectively and sequentially carrying out 'guest-two suspicion analysis', 'guest-two suspicion analysis' and 'guest-three suspicion analysis' on each vehicle in the suspicion library of the easily-confused vehicles, and then identifying the highest vehicle type in the analysis results as the suspect vehicle type of the vehicle;
the specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a second passenger, if so, continuously judging whether the frequency of the second passenger is more than or equal to n1, and if so, listing the second passenger in an analysis result;
the specific process of suspicion analysis of two guests and three guests is as follows: firstly, judging whether the vehicle type of the current vehicle contains a second passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n2, and if so, listing the third passenger in an analysis result;
the specific process of suspicion analysis of guest and guest is as follows: firstly, judging whether the type of the current vehicle contains a first passenger and a third passenger, if so, continuously judging whether the frequency of the third passenger is more than or equal to n3, and if so, listing the third passenger in an analysis result;
m, N, M1, M2, M3, N1, N2 and N3 are natural numbers respectively, M is more than 5, N is more than 1, M1 is more than 1, M2 is more than 1, M3 is more than 1, 1 is more than or equal to N1 and is less than M1, 1 is more than or equal to N2 and is less than or equal to M2, and 1 is more than or equal to N3 and is less than or equal to M3.
6. The confusing vehicle flight escape method according to claim 5, further comprising a pursuit sub-method, the pursuit sub-method comprising:
the station-level server analyzes and counts the toll payment condition of each confusable vehicle in the confusable vehicle formal library according to the confusable vehicle formal library and the charge data, and obtains the stealing and escaping charge information data of each confusable vehicle; when successful recollection of the evasion of the confusable vehicle, the station-level server records the evasion fee recollection successful event of the confusable vehicle, and marks and seals the evasion fee information data of the confusable vehicle.
7. The confusable vehicle-driving escape method as defined in claim 5, wherein M is 10, N is 2, M1 is M2 is 3, M3 is 2, and N1 is N2 is N3 is 1.
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| CN102110368B (en) * | 2009-12-25 | 2013-06-19 | 吕增及 | System and method for automatically checking vehicles evading toll fee by changing license plates on highways |
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