CN117312598A - Evidence obtaining method, device, computer equipment and storage medium for fee evasion auditing - Google Patents
Evidence obtaining method, device, computer equipment and storage medium for fee evasion auditing Download PDFInfo
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Abstract
The application relates to the technical field of highway auditing, and provides a evidence obtaining method, a device, computer equipment and a storage medium for fee evasion auditing. In the method, a passing identifier of a target vehicle to be subjected to fee evasion auditing in a target passing is obtained; acquiring non-image type traffic data of the target vehicle passing at the target time through the passing mark; obtaining an exit passing image of a target vehicle passing at a target time through a passing mark; determining a similar passing image according to the unique vector feature code of the exit passing image; obtaining image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image; and feeding back non-image and image type passing data of the target vehicle passing at the target time to the inspector. The method can automatically collect non-image and image type traffic data of the target vehicle passing at the target time, avoid missing the missing detection missing image type traffic data and provide more accurate fee escaping audit evidence.
Description
Technical Field
The present application relates to the field of highway auditing technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for collecting evidence of fee evasion auditing.
Background
Along with the network operation of the highway network, illegal vehicles can use various hidden forms to evade the toll, and the highway auditing works in massive multi-source data to obtain the evidence of the vehicle fee evasion, thereby striking various highway toll evasions.
However, the existing highway auditing needs to be manually collected in massive multi-source data by an inspector, cannot automatically obtain evidence, has the problems of high labor cost and low output effect, and particularly, when collecting image type traffic data, missing and missing detection are easy, and the highway traffic fee escaping behavior cannot be precisely and efficiently hit.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a forensic method, apparatus, computer device, computer-readable storage medium and computer program product for fee evasion auditing.
In a first aspect, the present application provides a method of forensic evidence evasion auditing, comprising:
acquiring a passing identifier of a target vehicle to be subjected to fee evasion auditing in a target passing;
acquiring non-image type traffic data of the target vehicle passing at the target time through the traffic identifier;
acquiring an exit passing image of the target vehicle passing at the target time through the passing identifier;
Determining a similar passing image with the similarity higher than a similarity threshold value according to the unique vector feature code of the exit passing image;
obtaining image type traffic data of the target vehicle passing through the target time according to the exit traffic image and the similar traffic image;
and feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to an inspector.
In one embodiment, before acquiring the traffic identifier of the target vehicle to be subjected to fee evasion audit in the target sub-traffic, the method further includes:
acquiring a vehicle set to be subjected to fee evasion auditing;
acquiring passing marks of vehicles passing at different times and corresponding non-image type passing data in the vehicle set;
and storing the traffic identification of the same vehicle in the same traffic and the non-image type traffic data in a database in a correlated way.
In one embodiment, before acquiring the traffic identifier of the target vehicle to be subjected to fee evasion audit in the target sub-traffic, the method further includes:
identifying and analyzing the full traffic images to obtain unique vector feature codes of all traffic images in the full traffic images;
Each pass image is stored in association with its own unique vector feature code in a database.
In one embodiment, the method further comprises:
acquiring an exit passing image and a passing identifier which are sent by an exit end of a highway and have an association relation;
and storing the exit passing image and the passing identifier with the association relationship into the database.
In one embodiment, the determining the similar passing image with the exit passing image having the similarity higher than the similarity threshold according to the unique vector feature code of the exit passing image includes:
obtaining a candidate vector feature code set according to the unique vector feature codes of the full traffic image;
respectively comparing the similarity between the export vector feature code and each candidate unique vector feature code in the candidate vector feature code set; the exit vector feature code is a unique vector feature code of the exit passing image;
the candidate unique vector feature codes with the similarity higher than the similarity threshold value are used as the similar vector feature codes;
and determining the pass image associated with the similar vector feature code as a similar pass image.
In one embodiment, the comparing the similarity between the exit vector feature code and each candidate unique vector feature code in the candidate vector feature code set includes:
Obtaining a cosine value according to the outlet vector feature codes and the candidate unique vector feature codes in the candidate vector feature code set;
and taking the cosine value as the similarity between the exit vector feature code and the candidate unique vector feature code.
In one embodiment, the obtaining the image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image includes:
if the number of the similar passing images is larger than a number threshold, acquiring the passing time of the expressway node associated with the passing identifier; the expressway node passing time comprises expressway portal passing time and expressway entrance passing time;
acquiring the image acquisition time of the similar passing images;
selecting similar passing images with the image acquisition time within a preset second before and after the passing time of the expressway node from the similar passing images to obtain target similar passing images;
and obtaining image type traffic data of the target vehicle passing through the target time according to the exit traffic image and the target similar traffic image.
In a second aspect, the present application further provides a forensic device for fee evasion audit, comprising:
The pass identifier acquisition module is used for acquiring a pass identifier of a target vehicle to be checked for fee evasion, which passes through the target at a target time;
the non-image data acquisition module is used for acquiring non-image type traffic data of the target vehicle passing at the target time through the passing identifier;
the similar image acquisition module is used for acquiring an exit passing image of the target vehicle passing at the target time through the passing identifier; determining a similar passing image with the similarity higher than a similarity threshold value according to the unique vector feature code of the exit passing image;
the image data acquisition module is used for acquiring image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image;
and the feedback module is used for feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to an inspector.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor executing the method described above.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which is executed by a processor to perform the above method.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which is executed by a processor to perform the above method.
The expressway fee evasion intelligent evidence obtaining method, the expressway fee evasion intelligent evidence obtaining device, the computer equipment, the storage medium and the computer program product acquire the passing identification of the target vehicle to be subjected to fee evasion auditing in the target passing; acquiring non-image type traffic data of the target vehicle passing at the target time through the passing mark; obtaining an exit passing image of a target vehicle passing at a target time through a passing mark; according to the unique vector feature code of the exit passing image, determining a similar passing image with the similarity higher than a similarity threshold value with the exit passing image; obtaining image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image; and feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to the inspector. According to the scheme, through the passing identifier, non-image type passing data and image type passing data of the target vehicle passing at the target time are obtained, automatic collection is achieved, and for the image type passing data, the exit passing image of the target vehicle passing at the target time is obtained through the passing identifier, according to the unique vector feature code of the exit passing image, a similar passing image with the similarity higher than the similarity threshold value is determined, the image type passing data of the target vehicle passing at the target time is obtained, missing omission is avoided, accurate fee evasion audit evidence is provided, and expressway passing fee evasion behavior can be hit accurately and effectively.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is an application environment diagram of a forensic method for fee evasion auditing in one embodiment;
FIG. 2 is a flow chart of a method of forensics for fee evasion audit in one embodiment;
FIG. 3 is a business schematic diagram of a forensic method of fee evasion auditing in one embodiment;
FIG. 4 is a schematic diagram of a method of forensics for fee evasion audit in one embodiment;
FIG. 5 is a flow chart of a method of evidence collection for fee evasion audit in another embodiment;
FIG. 6 is a block diagram of a forensic device for fee evasion auditing in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.
The evidence obtaining method for fee evasion auditing can be applied to an application environment shown in fig. 1. The expressway entrance end 101 collects entrance traffic images of vehicles at an expressway entrance, the expressway exit end 102 collects exit traffic images of vehicles at an expressway exit, and the lane end 103 collects portal traffic images of vehicles at an expressway portal. The highway entrance end 101, the highway exit end 102, and the lane end 103 communicate with the center end 104 through a network. The data storage system may store data that the central terminal 104 needs to process. The data storage system may be integrated on the central end 104 or may be located on a cloud or other network server. The center 104 acquires a passing identifier of a target vehicle to be subjected to fee evasion auditing in a target passing; acquiring non-image type traffic data of the target vehicle passing at the target time through the passing mark; obtaining an exit passing image of a target vehicle passing at a target time through a passing mark; according to the unique vector feature code of the exit passing image, determining a similar passing image with the similarity higher than a similarity threshold value with the exit passing image; obtaining image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image; and feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to the inspector.
In an exemplary embodiment, as shown in fig. 2, a method for evidence collection of fee evasion audit is provided, and the method is applied to the central end 104 in fig. 1 for illustration, and includes the following steps S201 to S206. Wherein:
step S201, a passing identifier of a target vehicle to be checked for fee evasion in the target pass is obtained.
The one-time passing path from the expressway entrance to the expressway exit is called one-time passing, the same vehicle can have multiple passes, each pass of the vehicle can have an associated pass identifier, the pass identifier can also be called a pass identifier ID, and whether the vehicle is the same or not, the pass identifiers associated with each pass are different. And (3) analyzing by an upstream auditing analysis system, wherein in the process of a certain traffic, if the certain vehicle is suspected to have the fee escaping behavior, the certain vehicle is called a target vehicle to be subjected to fee escaping auditing, and the traffic is called a target traffic.
In this step, after determining the target vehicle and the target secondary pass of the target vehicle, the inspector may trigger the upstream audit analysis system to send the pass identifier of the target vehicle passing at the target secondary pass to the central end.
Step S202, non-image type traffic data of the target vehicle passing at the target time is obtained through the traffic identification.
Non-image type traffic data includes vehicle traffic running water, vehicle identification running water, vehicle traffic path, and vehicle traffic log.
The non-image type traffic data of the vehicle in any traffic and the traffic identification of the vehicle in the traffic can be pre-associated and stored in a database, and the database can be a relational database. After the central terminal obtains the passing identifier of the target vehicle passing the target pass, the central terminal can obtain the non-image type passing data of the target vehicle passing the target pass through the passing identifier.
Step S203, obtaining an exit passing image of a target vehicle passing at a target time through a passing identifier;
cameras can be deployed at the entrance of the expressway and at the exit of the expressway, and cameras can be deployed at the portal of a road section between the entrance of the expressway and the exit of the expressway, so that a plurality of traffic images of the vehicle in the communication can be acquired in a single traffic process of the vehicle. Among a plurality of traffic images of the vehicle in each communication, a traffic image acquired by a camera disposed at an exit of a highway is referred to as an exit traffic image.
The plurality of traffic images of the same vehicle passing at different times and the plurality of traffic images of different vehicles passing at different times may be stored in the same database in advance, and the database may be a relational database.
The exit traffic image of the vehicle in any traffic can be associated with the traffic identifier of the vehicle in the traffic, and can be stored in a database in advance, so that the center end can acquire the exit traffic image of the target vehicle in the target traffic through the traffic identifier after acquiring the traffic identifier of the target vehicle in the target traffic.
Step S204, according to the unique vector feature code of the exit traffic image, the similar traffic image with the similarity higher than the similarity threshold value is determined.
The unique vector feature code is a feature representation method that can be used for image recognition and object recognition. The unique vector feature code is comprised of a set of vectors, each representing a local region of the image, containing pixel intensity, gradient, and texture information within the local region. The unique vector feature codes may capture tiny features in the image, such as edges and corner points. These features are very important for distinguishing between different objects and can reduce the complexity of the recognition calculation.
The center end carries out identification analysis on the exit passing image through an AI image analysis processing technology, extracts pixel intensity, gradient and texture information of the exit passing image, and forms a unique vector feature code of the exit passing image. Of course, the process of extracting the unique vector feature code of the exit traffic image may be implemented by an image calling service deployed at the exit end of the highway.
For other captured traffic images, the central terminal may extract unique vector feature codes for each traffic image.
After obtaining the unique vector feature codes of the traffic images, the central terminal can store the unique vector feature codes of the traffic images and the communication images in a database in advance, wherein the database can be a relational database.
After determining the exit traffic image of the target vehicle passing through the target time, the central terminal can determine a unique vector feature code associated with the exit traffic image in the database, and compare the unique vector feature code with other unique vector feature codes in the database, thereby determining traffic images which are similar to the exit traffic image and are higher than a similarity threshold in the database, wherein the traffic images can be called similar traffic images.
Step S205, obtaining image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image.
In this step, the exit traffic image and the similar traffic image may be used as image type traffic data of the target vehicle passing at the target time.
The image type passing data in the embodiment of the application comprises a picture type and a video type.
Step S206, non-image type traffic data and image type traffic data of the target vehicle passing at the target time are fed back to the inspector.
After obtaining the non-image type traffic data and the image type traffic data of the target vehicle in the target secondary traffic, the central terminal can take the data as the auditing evidence of whether the target vehicle has the fee escaping behavior in the target secondary traffic or not and feed the auditing evidence back to the auditing member.
The feedback mode can be a flow line detail, a vehicle passing path (a charging path, a recognition path and a fitting path), or a visual mode, wherein the visual mode comprises a static chart, a dynamic video, a two-dimensional map or a three-dimensional map, the feedback terminal can be a desktop terminal or a mobile terminal, the application is not limited to the visual mode and the selection of the feedback terminal, and the person skilled in the art can select according to practical application conditions.
According to the method for obtaining the evidence of the fee evasion audit, through the pass identifier, the non-image type pass data and the image type pass data of the target vehicle passing at the target time are obtained, automatic collection is achieved, and according to the image type pass data, the exit pass image of the target vehicle passing at the target time is obtained through the pass identifier, the similar pass image with the similarity higher than the similarity threshold value with the exit pass image is determined according to the unique vector feature code of the exit pass image, the image type pass data of the target vehicle passing at the target time is obtained, missing and missing of detection are avoided, more accurate fee evasion audit evidence is provided, and the expressway pass fee evasion behavior can be precisely and effectively hit.
The central side includes the data layer, the algorithm service layer and the business layer shown in fig. 3. The data layer realizes a data collection function, acquires networking charging data from the highway entrance end and the highway exit end, acquires license plate image videos from the highway entrance end, the highway exit end and the lane end, and can acquire lane logs from the lane end by using a lane log extraction service deployed at the lane end. The algorithm service layer realizes a data analysis function and cleans, analyzes and gathers the collected data. The business layer supports automatic evidence collection of fee evasion audit and supports graph search service.
The storage process of the non-image type traffic data and the image type traffic data at the central end can be seen in fig. 4, and the central end can collect the non-image type traffic data and the image type traffic data of multiple vehicles passing at different times from the entrance end of the highway, the lane end and the exit end of the highway, and the process can be called as a multi-source data collection process. The collected non-image type traffic data is stored in the road segment center database according to the data type (comprising the structured data type and the semi-structured data type), and the image type traffic data is stored in the road segment center database according to the non-structured data type.
Aiming at the vehicle set for fee evasion auditing: the center end can extract the non-image traffic type data and the image type traffic data of the vehicle set from the road section center database, and store the extracted non-image traffic type data and the corresponding traffic identification in the relational database in an associated manner; and extracting unique vector feature codes of the image type traffic data through identification analysis of the image and video data, converting the unique vector feature codes into structured data identification, and storing the image type traffic data and the unique vector feature codes in a relational database in an associated mode. Therefore, based on the data stored in the relational database, the central end can execute the steps S202 to S205, provide the automatic evidence obtaining service shown in fig. 5 for the inspector, and automatically collect the non-image type traffic data and the image type traffic data of the target vehicle passing at the target time; in addition, after obtaining the non-image type traffic data and the image type traffic data of the target vehicle passing at the target time, the central terminal can also be visually displayed to the inspector, so as to provide the visual display service shown in fig. 5 for the inspector.
With respect to storing the non-image type traffic data in the relational database, referring to fig. 5, the center side may extract the non-image type traffic data by the full text search technique and store the non-image type traffic data in the relational database. Regarding storing the image type traffic data in the relational database, referring to fig. 5, the central end may perform recognition analysis on the image type traffic data by using an image recognition technology to obtain unique vector feature codes of each traffic image in the image type traffic data, and use the unique vector feature codes as structured data identifiers of each traffic image, and store each traffic image and its own unique vector feature code in the relational database in an associated manner, so as to implement image data structuring.
Storing non-image type traffic data in a relational database, the application provides an embodiment in which, before acquiring a traffic identifier of a target vehicle to be charged for auditing at a target time, the method provided by the application further specifically includes the steps of: acquiring a vehicle set to be subjected to fee evasion auditing; acquiring passing marks of vehicles passing at different times and corresponding non-image type passing data; and storing the traffic identification of the same vehicle in the same traffic and the non-image type traffic data in a database in a correlated way.
The vehicle set to be checked for fee evasion comprises all suspected fee evasion vehicles which are researched and judged by the upstream checking and analyzing system.
In this embodiment, the center end uses the full text search technology to obtain the traffic identifier of each vehicle in the road section center database, and the corresponding non-image type traffic data, and the non-image type traffic data of any traffic and the traffic identifier of the vehicle in the traffic are associated and stored in the database, which may be a relational database, as shown in table 1. Therefore, the center end can obtain non-image type traffic data of the target vehicle passing at the target time through the traffic mark of the target vehicle passing at the target time, and data guarantee is made for evidence collection of fee evasion audit.
TABLE 1
Storing the image type traffic data in a relational database, the application provides an embodiment, in which, before acquiring the traffic identifier of the target vehicle to be checked for fee evasion at the target traffic, the method provided by the application further specifically includes the following steps: identifying and analyzing the full traffic images to obtain unique vector feature codes of all traffic images in the full traffic images; each pass image is stored in association with its own unique vector feature code in a database.
The full traffic image may include traffic images of vehicles collected at each pass to be checked for fee evasion; the center end can identify and analyze the full traffic images through the image structuring server by using an AI image analysis processing technology, and extract the pixel intensity, gradient and texture information of each traffic image in the full traffic images to form unique vector feature codes of each traffic image, so that the unique vector feature codes of each traffic image are obtained; each passing image is associated with its own unique vector feature code and stored in a relational database to identify the unique vector feature code as structured data for each passing image, as shown in table 2.
TABLE 2
In this embodiment, the central terminal stores each passing image and its own unique vector feature code in the database, so that the central terminal can determine the similar passing image with the similarity higher than the similarity threshold according to the unique vector feature code of the exit passing image, and can make data guarantee for the evidence collection of fee evasion audit.
In one embodiment, the method provided in the present application further specifically includes the following steps: acquiring an exit passing image and a passing identifier which are sent by an exit end of a highway and have an association relation; and storing the exit passage image and the passage identification with the association relationship into a database.
For example, when uploading the exit traffic image of the vehicle passing at any time to the central end, the highway exit end can upload the traffic identifier of the vehicle passing at the time to the central end, so that the central end can store the exit traffic image of the vehicle passing at the time and the traffic identifier in a database in a correlated manner.
In the embodiment, the central end stores the exit passing image and the passing identifier with the association relationship into the database, so that the exit passing image of the target vehicle passing at the target time can be conveniently obtained through the passing identifier, and data guarantee is made for the evidence collection of fee escaping auditing.
In one embodiment, according to the unique vector feature code of the exit traffic image, a similar traffic image with similarity to the exit traffic image higher than a similarity threshold is determined, and the specific steps are as follows: obtaining a candidate vector feature code set according to the unique vector feature codes of the full traffic image; respectively comparing the similarity between the outlet vector feature codes and each candidate unique vector feature code in the candidate vector feature code set; the exit vector feature code is the unique vector feature code of the exit passing image; the candidate unique vector feature codes with the similarity higher than the similarity threshold value are used as the similar vector feature codes; the pass image associated with the similar vector feature code is determined to be a similar pass image.
The similarity threshold may be set according to actual auditing conditions.
The unique vector feature codes of the full-quantity passing image are used as candidate unique vector feature codes, a candidate vector feature code set is obtained, the similarity between the exit vector feature codes and the candidate unique vector feature codes in the candidate vector feature code set is respectively compared, and the candidate unique vector feature codes with the similarity higher than a similarity threshold value are used as similar vector feature codes; the pass image associated with the similar vector feature code is determined to be a similar pass image.
In this embodiment, the similarity between the exit vector feature code and each candidate unique vector feature code in the candidate vector feature code set is compared, and the candidate unique vector feature code with the similarity higher than the similarity threshold is used as the similarity vector feature code, and the traffic image associated with the similarity vector feature code is determined as the similarity traffic image, so that the similar traffic image of the target vehicle at the target time is obtained through the exit traffic image of the target vehicle at the target time.
In one embodiment, the similarity between the exit vector feature code and each candidate unique candidate vector feature code in the candidate vector feature code set is compared, and the specific steps are as follows: obtaining a cosine value according to the outlet vector feature codes and the candidate unique vector feature codes in the candidate vector feature code set; and taking the cosine value as the similarity between the exit vector feature code and the candidate unique vector feature code.
And obtaining cosine values of the candidate unique vector feature codes in the exit vector feature codes and the candidate vector feature code sets according to a cosine formula of the vector, and taking the cosine values as the similarity between the exit vector feature codes and the candidate unique vector feature codes.
In this embodiment, cosine values of the unique candidate vector feature codes in the exit vector feature code and the candidate vector feature code set are obtained through a cosine formula of the vector, and the cosine values are used as the similarity between the exit vector feature code and the unique candidate vector feature code, so that the similarity between the exit vector feature code and the unique candidate vector feature code is obtained.
In one embodiment, the image type traffic data of the target vehicle passing at the target time is obtained according to the exit traffic image and the similar traffic image, and the specific steps are as follows: if the number of the similar passing images is larger than the number threshold, acquiring the passing time of the expressway nodes associated with the passing identifiers; the expressway node passing time comprises expressway portal passing time and expressway entrance passing time; acquiring the image acquisition time of a similar passing image; selecting similar traffic images with image acquisition time within a preset second before and after the traffic time of the expressway node from the similar traffic images to obtain target similar traffic images; and obtaining image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the target similar traffic image.
The number threshold may be set according to the number of similar pass images in actual cases.
The preset seconds may be set according to actual conditions.
The expressway node at least comprises an expressway portal and an expressway entrance, and the expressway node passing time at least comprises expressway portal passing time and expressway entrance passing time.
If the number of the similar pass images is greater than the number threshold, error data exists in the similar pass images with high probability, such as image type pass data which does not belong to the target vehicle passing at the target time or image type pass data of the non-target vehicle, so that the subsequent auditing and evidence obtaining efficiency of the auditor is affected, and when the number of the similar pass images is greater than the number threshold, the similar pass images can be further screened.
When the number of the similar passing images is larger than the number threshold, the central end can acquire the passing time of the expressway nodes associated with the passing identifiers from the relational database, and in addition, the central end can acquire the image acquisition time of the similar passing images from the relational database; and taking the similar passing images with the image acquisition time within a preset second before and after the passing time of the expressway node as the target similar passing images to obtain the target similar passing images. And taking the exit passing image and the target similar passing image as image type passing data of the target vehicle passing at the target time to obtain image type passing data of the target vehicle passing at the target time.
Illustratively, the central end may obtain traffic node traffic times associated with traffic identifications from a relational database, including 10 minutes 30 seconds, 10 minutes 20 minutes 30 seconds, and 10 minutes 30 seconds for the portal traffic time, and 1 minute 30 seconds for the portal traffic time 10; in addition, the center terminal may acquire the image acquisition time of each of the similar pass images from the relational database, wherein the image acquisition time of the similar pass image 1 is 1 minute 20 seconds, the image acquisition time of the similar pass image 2 is 10 minutes 20 seconds, the image acquisition time of the similar pass image 3 is 10 minutes 50 seconds, the image acquisition time of the similar pass image 4 is 10 minutes 20 minutes 40 seconds, the image acquisition time of the similar pass image 5 is 10 minutes 20 minutes 47 seconds, and the image acquisition time of the similar pass image 6 is 10 minutes 30 minutes 25 seconds, and the similar pass image whose image acquisition time is within 15 seconds before and after the passing time of the expressway node is taken as the target similar pass image, and the target similar pass image thus obtained includes the similar pass image 1, the similar pass image 2, the similar pass image 4, and the similar pass image 6. And taking the exit passing image and the target similar passing image as image type passing data of the target vehicle passing at the target time to obtain image type passing data of the target vehicle passing at the target time.
When the number of the similar pass images is smaller than or equal to the number threshold, the similar pass images can be directly used as image type pass data of the target vehicle passing at the target time without further screening the similar pass images.
In this embodiment, when the number of the similar pass images is greater than the number threshold, the similar pass images with the image acquisition time within a preset second before and after the passing time of the expressway node are used as the target similar pass images, and the exit pass images and the target similar pass images are used as the image type passing data of the target vehicles passing at the target time, so that the efficiency of collecting evidence of the fare audit of the target vehicles is improved.
In one embodiment, the corresponding extraction of the traffic data may be performed according to the differential requirements of different fare evasions for the content of the audit evidence.
The behavior of the fee-escaping vehicle can be divided into four fee-escaping types, including changing the type of vehicle (type of vehicle), changing the fee-escaping type of the payment path, using the policy-free fee-escaping type and other fee-escaping types due to less, no traffic fee-escaping from customers. The requirements for audit evidence are different for each escape type. The fare evasion type of the target vehicle to be fare evasion may be acquired from the upstream auditing analysis system.
Illustratively, changing the type of the fee evasion of the vehicle type (vehicle type) focuses on the audit evidence that the fee evasion vehicle passes by the low-rate vehicle type, the required audit evidence focuses on the image of the entrance vehicle to prove the real vehicle type and the portal/exit running charging vehicle type, when the fee evasion vehicle of the type is subjected to the evidence collection of the fee evasion inspection, the image type traffic data can be correspondingly extracted, the changing the fee evasion type focuses on the difference between the real running path and the actual charging path of the vehicle, focuses on the comparison of the charging information of the portal along the way with the brand identification information, and when the fee evasion vehicle of the type is subjected to the evidence collection of the fee evasion inspection, the non-image type traffic data can be correspondingly extracted.
In the embodiment, the corresponding extraction of the traffic data can be performed according to the difference requirements of different fee evasion types on the content of the audit evidence, so that the accuracy of evidence collection of fee evasion audit of the current vehicle is improved.
In one embodiment, when the license plate number is blurred or the number is sleeved in the image of the target vehicle to be checked for fee evasion, the image search service can be provided.
The image searching service can acquire the traffic data of the type of the target similar image with the similarity higher than the similarity threshold value according to the unique vector feature code of the image of the target vehicle through the image of the target vehicle. If the number of the image type traffic data is larger than a number threshold value, acquiring the traffic time of the expressway node associated with the image type traffic data; the expressway node passing time comprises expressway portal passing time and expressway entrance passing time; acquiring the image acquisition time of the image type traffic data; and selecting similar image type traffic data with image acquisition time within a preset second before and after the traffic time of the expressway node from the image type traffic data to obtain target similar image type traffic data.
When the target similar image type traffic data comprises image type traffic data with clear visible license plate numbers or no fake license plate behaviors, the license plate numbers of the target vehicles can be obtained, and the traffic marks of the target vehicles passing at the target times are obtained through the license plate numbers of the target vehicles, so that non-image type traffic data of the target vehicles passing at the target times are obtained.
According to the embodiment, the map search service is provided, so that the traffic data of the similar image type of the target vehicle can be accurately found.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a evidence obtaining device for realizing the evidence obtaining method for fee evasion audit. The implementation scheme of the solution provided by the device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiments of the evidence obtaining device for fee evasion audit provided below can be referred to the limitation of the evidence obtaining method for fee evasion audit hereinabove, and will not be repeated here.
In one exemplary embodiment, as shown in fig. 6, a forensic device for fee evasion auditing is provided, wherein:
the pass identifier obtaining module 601 is configured to obtain a pass identifier of a target vehicle to be checked for fee evasion when passing through the target vehicle;
the non-image data obtaining module 602 is configured to obtain non-image type traffic data of the target vehicle passing through the target time according to the passing identifier;
a similar image obtaining module 603, configured to obtain, according to the traffic identifier, an exit traffic image of the target vehicle that passes through the target at the second time; determining a similar passing image with the similarity higher than a similarity threshold value according to the unique vector feature code of the exit passing image;
An image data obtaining module 604, configured to obtain image type traffic data of the target vehicle passing through the target according to the exit traffic image and the similar traffic image;
and the feedback module 605 is used for feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to an inspector.
In one embodiment, the data storage module is configured to: acquiring a vehicle set to be subjected to fee evasion auditing; acquiring passing marks of vehicles passing at different times and corresponding non-image type passing data in the vehicle set; and storing the traffic identification of the same vehicle in the same traffic and the non-image type traffic data in a database in a correlated way.
In one embodiment, the data storage module is further configured to: identifying and analyzing the full traffic images to obtain unique vector feature codes of all traffic images in the full traffic images; each pass image is stored in association with its own unique vector feature code in a database.
In one embodiment, the data storage module is further configured to: acquiring an exit passing image and a passing identifier which are sent by an exit end of a highway and have an association relation; and storing the exit passing image and the passing identifier with the association relationship into the database.
In one embodiment, the similar image acquisition module 603 is further configured to: obtaining a candidate vector feature code set according to the unique vector feature codes of the full traffic image; respectively comparing the similarity between the export vector feature code and each candidate unique vector feature code in the candidate vector feature code set; the exit vector feature code is a unique vector feature code of the exit passing image; the candidate unique vector feature codes with the similarity higher than the similarity threshold value are used as the similar vector feature codes; and determining the pass image associated with the similar vector feature code as a similar pass image.
In one embodiment, the similar image acquisition module 603 is further configured to: obtaining a cosine value according to the outlet vector feature codes and the candidate unique vector feature codes in the candidate vector feature code set; and taking the cosine value as the similarity between the exit vector feature code and the candidate unique vector feature code.
In one embodiment, the image data acquisition module 604 is further configured to: if the number of the similar passing images is larger than a number threshold, acquiring the passing time of the expressway node associated with the passing identifier; the expressway node passing time comprises expressway portal passing time and expressway entrance passing time; acquiring the image acquisition time of the similar passing images; selecting similar passing images with the image acquisition time within a preset second before and after the passing time of the expressway node from the similar passing images to obtain target similar passing images; and obtaining image type traffic data of the target vehicle passing through the target time according to the exit traffic image and the target similar traffic image.
All or part of each module in the evidence obtaining device for fee evasion audit can be realized by software, hardware and the combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In an exemplary embodiment, a computer apparatus is provided, through which the above-described central terminal may be implemented, and an internal structural diagram thereof may be as shown in fig. 7. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data of the evidence obtaining method of fee evasion audit. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a forensic method of fee evasion auditing.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of forensic evidence of fee evasion auditing, the method comprising:
acquiring a passing identifier of a target vehicle to be subjected to fee evasion auditing in a target passing;
acquiring non-image type traffic data of the target vehicle passing at the target time through the traffic identifier;
acquiring an exit passing image of the target vehicle passing at the target time through the passing identifier;
Determining a similar passing image with the similarity higher than a similarity threshold value according to the unique vector feature code of the exit passing image;
obtaining image type traffic data of the target vehicle passing through the target time according to the exit traffic image and the similar traffic image;
and feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to an inspector.
2. The method of claim 1, wherein prior to obtaining a pass identification of a target vehicle to be evaluable for auditing at a target pass, the method further comprises:
acquiring a vehicle set to be subjected to fee evasion auditing;
acquiring passing marks of vehicles passing at different times and corresponding non-image type passing data in the vehicle set;
and storing the traffic identification of the same vehicle in the same traffic and the non-image type traffic data in a database in a correlated way.
3. The method of claim 1, wherein prior to obtaining a pass identification of a target vehicle to be evaluable for auditing at a target pass, the method further comprises:
identifying and analyzing the full traffic images to obtain unique vector feature codes of all traffic images in the full traffic images;
Each pass image is stored in association with its own unique vector feature code in a database.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring an exit passing image and a passing identifier which are sent by an exit end of a highway and have an association relation;
and storing the exit passing image and the passing identifier with the association relationship into the database.
5. The method of claim 1, wherein the determining a similar pass image having a similarity to the exit pass image above a similarity threshold based on the unique vector feature code of the exit pass image comprises:
obtaining a candidate vector feature code set according to the unique vector feature codes of the full traffic image;
respectively comparing the similarity between the export vector feature code and each candidate unique vector feature code in the candidate vector feature code set; the exit vector feature code is a unique vector feature code of the exit passing image;
the candidate unique vector feature codes with the similarity higher than the similarity threshold value are used as the similar vector feature codes;
and determining the pass image associated with the similar vector feature code as a similar pass image.
6. The method of claim 5, wherein comparing the similarity between the exit vector feature code and each candidate unique vector feature code in the set of candidate vector feature codes, respectively, comprises:
obtaining a cosine value according to the outlet vector feature codes and the candidate unique vector feature codes in the candidate vector feature code set;
and taking the cosine value as the similarity between the exit vector feature code and the candidate unique vector feature code.
7. The method of claim 1, wherein the obtaining image type traffic data of the target vehicle at the target secondary pass from the exit traffic image and the similar traffic image comprises:
if the number of the similar passing images is larger than a number threshold, acquiring the passing time of the expressway node associated with the passing identifier; the expressway node passing time comprises expressway portal passing time and expressway entrance passing time;
acquiring the image acquisition time of the similar passing images;
selecting similar passing images with the image acquisition time within a preset second before and after the passing time of the expressway node from the similar passing images to obtain target similar passing images;
And obtaining image type traffic data of the target vehicle passing through the target time according to the exit traffic image and the target similar traffic image.
8. A forensic device for fee evasion auditing, the device comprising:
the pass identifier acquisition module is used for acquiring a pass identifier of a target vehicle to be checked for fee evasion, which passes through the target at a target time;
the non-image data acquisition module is used for acquiring non-image type traffic data of the target vehicle passing at the target time through the passing identifier;
the similar image acquisition module is used for acquiring an exit passing image of the target vehicle passing at the target time through the passing identifier; determining a similar passing image with the similarity higher than a similarity threshold value according to the unique vector feature code of the exit passing image;
the image data acquisition module is used for acquiring image type traffic data of the target vehicle passing at the target time according to the exit traffic image and the similar traffic image;
and the feedback module is used for feeding back non-image type traffic data and image type traffic data of the target vehicle passing at the target time to an inspector.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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