CN113569899A - Intelligent classification method for fee stealing and evading behaviors, storage medium and terminal - Google Patents
Intelligent classification method for fee stealing and evading behaviors, storage medium and terminal Download PDFInfo
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- CN113569899A CN113569899A CN202110626178.7A CN202110626178A CN113569899A CN 113569899 A CN113569899 A CN 113569899A CN 202110626178 A CN202110626178 A CN 202110626178A CN 113569899 A CN113569899 A CN 113569899A
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Abstract
The invention provides an intelligent classification method for evasion fee behaviors, a storage medium and a terminal, belonging to the field of highway charging, wherein the method comprises the following steps: historical stealing and escaping fee cases and normal passing cases are collected, a stealing and escaping fee occurrence and type judgment rule model is established, and the model is verified and applied. And associating the final rule model for determining the occurrence and type of the evasion fee with a high-speed station charging system, monitoring the real-time passing vehicles, determining whether the evasion fee behavior occurs or not, determining the type of the evasion fee, and finally, visually displaying the monitoring result to a worker. The method introduces a machine learning technology, and utilizes the extracted features to train a machine learning model in the model through algorithms such as random forest, LASSO, decision tree, logistic regression, GBDT and the like to generate a machine learning model for correspondingly detecting whether the evasion fee occurs or not and judging the type, and screens new abnormal features in the application of the model, artificially judges and adds new types, thereby improving the learning capability and the application scope of the model.
Description
Technical Field
The invention relates to a highway fee evasion vehicle inspection technology, in particular to an intelligent classification method for fee evasion behaviors, a storage medium and a terminal.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art or the prior art.
Along with highway road network constantly enlarges, some illegal vehicles utilize various forms to steal the toll, and the fee evasion form is various and disguised strong, mainly includes ETC fee evasion, CPC fee evasion, impersonation free fee evasion, applies for a plurality of OBUs and ETC card, screen door clamp signal simultaneously and realizes minimum rate etc..
This disturbs the normal order of highway toll collection and causes huge economic loss. In order to maintain the normal charging order and the fair payment environment of the expressway, the toll collection management is further enhanced, the toll behavior of evading and leaking is effectively prevented and restrained, and the strict legal charging according to the charging standard is ensured, which is urgent.
Although management departments actively adopt some control means, effective solutions are still lacked in practice, and particularly, an efficient method is not available in the link of actively finding vehicles suspected of stealing and escaping toll, the efficient method is usually carried out by a manual inquiry mode, and the effect is extremely low. After the provincial toll stations are cancelled nationwide and a portal frame sectional charging mode is adopted, on one hand, the original escape mechanism and the escape tools fail to work; on the other hand, after the data of the door frame is added, the data volume is greatly increased, and the traditional prevention and control system is not a big data and artificial intelligence architecture and is difficult to continue to be used normally. In addition, the stealing and escaping method is changed, and the original prevention and control technology is basically in the state of paralysis reconstruction.
The prevention and control measures for the toll evasion behavior in the current stage of the highway mainly comprise the following steps:
the method has the following disadvantages: mainly, manual inspection is used as the main point: the method greatly depends on the experience of field toll collector and inspection personnel;
the method has the following disadvantages: at present, an effective means of active discovery is lacked, and the conventional auditing system can certainly catch a plurality of abnormal vehicles every day by deploying rules formulated by provincial requirements, but the accuracy is not high and the auditing workload is very large because the conditions and the characteristics of each road section are different and the data quality is gradually improved.
The disadvantages are three: the inspectors in all road sections cannot configure rules independently, and cannot give full play to the first-line business experience of the inspectors in all road section companies, so that the inspectors cannot be put into the link of actively finding the fee evasion vehicles efficiently.
The defect is four: after-the-fact inspection is the main, no early warning exists in the process: after-investigation, a lot of important information is lost, so that the evidence is difficult to obtain, the cost is difficult to recover, and the stealing behavior cannot be effectively deterred.
Therefore, there is an urgent need for a method for efficiently classifying evasion fee cases.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an intelligent classification method for fee evasion behaviors, a storage medium and a terminal, which can solve the problems.
An intelligent classification method for highway toll evasion behaviors based on big data analysis comprises the following steps:
s1, collecting historical stealing and evading fee cases and normal passing cases;
s2, analyzing the stealing and escaping fee case and the normal passing case, comparing, analyzing and extracting stealing and escaping characteristics, and establishing a stealing and escaping fee occurrence and type judgment rule model;
s3, model verification, namely calling different evasion fee cases as verification samples in the step S2 to enter a rule model, manually verifying an output result, judging the accuracy of the model, taking the result as a final rule model if the accuracy of the model reaches a precision permission threshold, searching evasion fee cases corresponding to the problem if the accuracy of the model is lower than the precision permission threshold, re-analyzing the evasion fee cases of the same type to extract characteristics, optimizing the evasion fee generation and type judgment rule model until the precision threshold reaches the permission threshold, and completing the model verification;
and S4, model application, namely associating the final stealing and evading fee occurrence and type judgment rule model with a high-speed station charging system, monitoring the real-time passing vehicles, judging whether stealing and evading fee behaviors occur or not, judging the stealing and evading fee types, and finally, visually displaying the monitoring result to a worker.
Preferably, the establishment of the evasion fee occurrence and type determination rule model includes the following steps:
s21, taking the extracted evasion features as feature parameters for evasion fee judgment;
s22, establishing a characteristic parameter and evasion fee type judgment relation, and setting a parameter threshold value for the characteristic parameter;
and S23, carrying out sample training through the historical stealing and escaping fee case and the normal passing case to complete model establishment.
Preferably, during the model operation process, the method further comprises the following steps:
s24, screening the new abnormal characteristic cases, extracting new characteristics, and manually setting the type of the evasion fee corresponding to the new characteristics.
The present invention also provides a computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of the aforementioned method.
The invention also provides a terminal which comprises a memory and a processor, wherein the memory is stored with standard information, historical information, real-time information and a computer instruction capable of running on the processor, the standard information comprises a toll collection standard case and a normal traffic case, the historical information comprises traffic vehicle monitoring information and police system memorandum special vehicle information in a certain time, the real-time information comprises real-time monitored traffic vehicle video information, and the processor executes the steps of the method when running the computer instruction.
Compared with the prior art, the invention has the beneficial effects that: the method introduces a machine learning technology, machine learning model training is carried out on the models by using the extracted features through an algorithm, corresponding machine learning models for detecting whether the surcharge is generated or not and judging the types are generated, new abnormal features are screened in model application, new types are artificially judged and added, and the learning ability, the judging efficiency, the judging accuracy and the application breadth of the models are improved.
Drawings
FIG. 1 is a flow chart of an intelligent classification method for highway toll evasion behavior based on big data analysis according to the present invention;
FIG. 2 is a schematic diagram of a model application update process.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
First embodiment
An intelligent classification method for highway toll evasion behaviors based on big data analysis is disclosed, and referring to fig. 1, the method comprises the following steps.
And S1, collecting historical stealing fee cases and normal passing cases.
The step S1 of obtaining the historical fee stealing and evading cases and the normal passing cases includes collecting the words and pictures related to the fee stealing and evading cases through the network, and obtaining the words and pictures through communicating with the inspectors of the highway management centers.
And S2, analyzing the stealing and escaping fee case and the normal passing case, comparing, analyzing and extracting stealing and escaping characteristics, and establishing a stealing and escaping fee occurrence and type judgment rule model.
The normal traffic case of step S2 includes that the vehicle has a correct entrance, exit and/or correct payment. The evasion fee case extraction features include fake car class, ETC class and CPC class, and wherein the fake car class includes fake military vehicle and the green car that leads to of fake, the ETC class includes that the car card is inconsistent, the car is signed the displacement, a car is signed many, the vehicle interference, is detained entry information, the motorcycle type is inconsistent and break through the mouth, CPC class includes the truck of falling, trades the truck, false truck, U/J type line evasion fee, export break through the truck and disturb the evasion fee, wherein the car of falling includes that export motorcycle type and license plate are inconsistent, the overtime of the near distance, the transfinite of the near distance, the empty load of the far distance and the car pass through from the access & exit.
The establishment of the stealing fee occurrence and type judgment rule model comprises the following steps:
and S21, taking the extracted evasion characteristics as characteristic parameters for evasion fee judgment.
S22, establishing a characteristic parameter and evasion fee type judgment relation, and setting a parameter threshold value for the characteristic parameter.
And S23, carrying out sample training through the historical stealing and escaping fee case and the normal passing case to complete model establishment.
During the operation of the model, referring to fig. 2, the following steps are also included:
s24, screening the new abnormal characteristic cases, extracting new characteristics, and manually setting the type of the evasion fee corresponding to the new characteristics.
And S3, performing model verification, calling the different evasion fee cases as verification samples in the step S2, inputting the different evasion fee cases into the rule model, performing manual verification on the output result, judging the accuracy of the model, taking the different evasion fee cases as the verification samples, searching evasion fee cases corresponding to the problem if the accuracy of the output result reaches the accuracy permission threshold, re-analyzing the evasion fee cases of the same type for feature extraction, optimizing the evasion fee generation and type judgment rule model until the accuracy threshold reaches the permission threshold, and completing the model verification.
And S4, model application, namely associating the final stealing and evading fee occurrence and type judgment rule model with a high-speed station charging system, monitoring the real-time passing vehicles, judging whether stealing and evading fee behaviors occur or not, judging the stealing and evading fee types, and finally, visually displaying the monitoring result to a worker.
The method introduces a machine learning technology, performs machine learning model training by using the extracted features through algorithms such as random forest, LASSO, decision tree, logistic regression, GBDT and the like in the model, generates a corresponding machine learning model for detecting whether the evasion fee occurs or not and judging the type, screens new abnormal features in the model application, artificially judges and adds new types, and improves the learning capability and the application range of the model.
Second embodiment
The present invention also provides a computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of the aforementioned method. For details, the method is described in the foregoing section, and is not repeated here.
It will be appreciated by those of ordinary skill in the art that all or a portion of the steps of the various methods of the embodiments described above may be performed by associated hardware as instructed by a program that may be stored on a computer readable storage medium, which may include non-transitory and non-transitory, removable and non-removable media, to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visualbasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Third embodiment
A terminal comprises a memory and a processor, wherein standard information, historical information, real-time information and a computer instruction capable of running on the processor are stored in the memory, the standard information comprises a toll standard case and a normal passing case, the historical information comprises passing vehicle monitoring information and police system memorandum special vehicle information in a certain time, the real-time information comprises passing vehicle video information monitored in real time, and the processor executes the steps of the method when running the computer instruction. For details, the method is described in the foregoing section, and is not repeated here.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An intelligent classification method for highway toll evasion behaviors based on big data analysis is characterized by comprising the following steps:
s1, collecting historical stealing and evading fee cases and normal passing cases;
s2, analyzing the stealing and escaping fee case and the normal passing case, comparing, analyzing and extracting stealing and escaping characteristics, and establishing a stealing and escaping fee occurrence and type judgment rule model;
s3, model verification, namely calling different evasion fee cases as verification samples in the step S2 to enter a rule model, manually verifying an output result, judging the accuracy of the model, taking the result as a final rule model if the accuracy of the model reaches a precision permission threshold, searching evasion fee cases corresponding to the problem if the accuracy of the model is lower than the precision permission threshold, re-analyzing the evasion fee cases of the same type to extract characteristics, optimizing the evasion fee generation and type judgment rule model until the precision threshold reaches the permission threshold, and completing the model verification;
and S4, model application, namely associating the final stealing and evading fee occurrence and type judgment rule model with a high-speed station charging system, monitoring the real-time passing vehicles, judging whether stealing and evading fee behaviors occur or not, judging the stealing and evading fee types, and finally, visually displaying the monitoring result to a worker.
2. The intelligent classification method for high-speed road toll evasion behaviors according to claim 1, characterized in that: the step S1 of obtaining the historical fee stealing and evading cases and the normal passing cases includes collecting the words and pictures related to the fee stealing and evading cases through the network, and obtaining the words and pictures through communicating with the inspectors of the highway management centers.
3. The intelligent classification method for highway evasion fee behaviors according to claim 1 or 2, characterized in that: the normal traffic case of step S2 includes that the vehicle has a correct entrance, exit and/or correct payment.
4. The intelligent classification method for highway evasion fee behaviors according to claim 1 or 2, characterized in that: the evasion fee case extraction features include fake car class, ETC class and CPC class, and wherein the fake car class includes fake military vehicle and the green car that leads to of fake, the ETC class includes that the car card is inconsistent, the car is signed the displacement, a car is signed many, the vehicle interference, is detained entry information, the motorcycle type is inconsistent and break through the mouth, CPC class includes the truck of falling, trades the truck, false truck, U/J type line evasion fee, export break through the truck and disturb the evasion fee, wherein the car of falling includes that export motorcycle type and license plate are inconsistent, the overtime of the near distance, the transfinite of the near distance, the empty load of the far distance and the car pass through from the access & exit.
5. The intelligent classification method for high-speed road fee evasion behaviors according to claim 1, wherein the establishment of the fee evasion occurrence and type judgment rule model comprises the following steps:
s21, taking the extracted evasion features as feature parameters for evasion fee judgment;
s22, establishing a characteristic parameter and evasion fee type judgment relation, and setting a parameter threshold value for the characteristic parameter;
and S23, carrying out sample training through the historical stealing and escaping fee case and the normal passing case to complete model establishment.
6. The intelligent classification method for high-speed road toll evasion behaviors according to claim 5, characterized by further comprising the following steps in the model operation process:
s24, screening the new abnormal characteristic cases, extracting new characteristics, and manually setting the type of the evasion fee corresponding to the new characteristics.
7. A computer-readable storage medium having stored thereon computer instructions, characterized in that: the computer instructions when executed perform the method of any of claims 1-6.
8. A terminal comprises a memory and a processor, wherein standard information, historical information, real-time information and computer instructions capable of running on the processor are stored in the memory, the standard information comprises fee stealing and passing standard cases and normal passing cases, the historical information comprises passing vehicle monitoring information and public security system memorandum special vehicle information in a certain past time, and the real-time information comprises passing vehicle video information monitored in real time, and the terminal is characterized in that: the processor, when executing the computer instructions, performs the method of any of claims 1-6.
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| CN116129541A (en) * | 2022-12-28 | 2023-05-16 | 广州天长信息技术有限公司 | Fee evasion checking method, device and equipment based on generalized additive model |
| CN116630903A (en) * | 2022-12-29 | 2023-08-22 | 北京中科神通科技有限公司 | Method and system for detecting behavior fee evasion of highway counterfeit bus |
| CN117558071A (en) * | 2024-01-11 | 2024-02-13 | 四川成渝高速公路股份有限公司 | Expressway vehicle access checking method and system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116129541A (en) * | 2022-12-28 | 2023-05-16 | 广州天长信息技术有限公司 | Fee evasion checking method, device and equipment based on generalized additive model |
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| CN117558071A (en) * | 2024-01-11 | 2024-02-13 | 四川成渝高速公路股份有限公司 | Expressway vehicle access checking method and system |
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Application publication date: 20211029 |