Vehicle fee evasion and omission auditing method and device

Abstract

The invention discloses a vehicle fee evasion and omission auditing method and device, and relates to the field of road data checking, in order to solve the technical problem that the accuracy of vehicle fee evasion and omission auditing is not high, and the method comprises the steps: obtaining vehicle passing data of a plurality of toll stations and a plurality of highway sections, carrying out the feature extraction of the vehicle passing data, and carrying out the feature extraction of the vehicle passing data; obtaining a passing vehicle feature set; segmenting the passing vehicle feature set based on a preset passing feature mark to obtain a station-crossing vehicle feature set and a road network vehicle feature set; based on a preset feature difference scoring method, according to the station-crossing vehicle feature set and the road network vehicle feature set, obtaining a first fee evasion and leakage vehicle feature set; based on a preset road network model, screening the first fee evasion and leakage vehicle feature set to obtain a second fee evasion and leakage vehicle feature set; and according to the vehicle passing data, calculating the highway fee to be supplemented of each vehicle in the second fee evasion and omission vehicle feature set, so as to perform vehicle fee evasion and omission auditing according to the highway fee to be supplemented.

Description

Technical Field

[0001] The present application relates to the field of highway data verification, and in particular to a method and device for verifying vehicle fee evasion. Background Art

[0002] With the continuous construction and development of expressways, the expressway network has closely connected the traffic of various provinces. Along with this comes the inspection and analysis of a large number of inter-provincial vehicles, especially the inspection of vehicles evading fees among inter-provincial vehicles. At present, there are two main difficulties in the inspection of inter-provincial vehicles evading fees: first, it is difficult to identify similar vehicles, which is easy to cause misjudgment and the accuracy of the audit will also decrease; second, in the process of inter-provincial travel, it is difficult to correctly identify vehicles that evade fees through false license plates while excluding legal changes in vehicle license plates, and the audit is difficult. Therefore, how to improve the accuracy of vehicle evasion audits is still a problem that needs to be solved urgently in the existing technology. Summary of the invention

[0003] The present application provides a method and device for auditing vehicle fee evasion to solve the technical problem of low accuracy in auditing vehicle fee evasion.

[0004] According to a first aspect of the implementation manner of the present application, a vehicle fee evasion and fee verification method is provided, comprising:

[0005] Acquire vehicle traffic data of multiple toll stations and multiple highway sections, and perform feature extraction on the vehicle traffic data to obtain a feature set of passing vehicles; wherein the multiple highway sections are located between the multiple toll stations; the vehicle traffic data includes vehicle traffic time, vehicle identification features, and a plurality of frames of vehicle traffic images;

[0006] Based on the preset passing feature markers, the passing vehicle feature set is segmented to obtain a passing vehicle feature set and a road network vehicle feature set;

[0007] Based on a preset feature difference scoring method, a first toll evasion vehicle feature set is obtained according to the transit vehicle feature set and the road network vehicle feature set;

[0008] Based on a preset road network model, the first toll evasion vehicle feature set is screened to obtain a second toll evasion vehicle feature set;

[0009] The highway toll payable of each vehicle in the second toll evasion vehicle feature set is calculated based on the vehicle traffic data, so as to conduct a vehicle toll evasion audit based on the toll payable toll.

[0010] The present application first extracts features from vehicle traffic data to obtain a passing vehicle feature set, then divides the passing vehicle feature set to obtain a passing vehicle feature set and a road network vehicle feature set, and then obtains a first toll evasion vehicle feature set based on a feature difference scoring method. By comparing the feature differences between vehicles passing through toll stations and vehicles passing through expressway sections, the recognition accuracy of similar vehicles can be improved and the probability of misjudgment can be reduced. The first toll evasion vehicle feature set is then screened based on a road network model to obtain a second toll evasion vehicle feature set. Based on the road network model, some normal passing vehicles that are not toll evasion can be screened out, thereby improving the accuracy of vehicle toll evasion audits when calculating the tolls that should be paid for each vehicle in the second toll evasion vehicle feature set for vehicle toll evasion audits.

[0011] In certain embodiments of the present application, extracting features from the vehicle traffic data to obtain a traffic vehicle feature set specifically includes:

[0012] According to the deep learning-based image segmentation method, a plurality of frames of vehicle traffic images of each vehicle in the vehicle traffic data are segmented to obtain vehicle color features, vehicle model features, and vehicle damage features of each vehicle;

[0013] Normalize and concatenate the vehicle color feature, vehicle model feature, and vehicle damage feature of each vehicle to obtain the first comprehensive feature of each vehicle;

[0014] Based on a preset standard, the first comprehensive feature of each vehicle is normalized to obtain a second comprehensive feature of each vehicle;

[0015] A passing vehicle feature set is obtained according to the second comprehensive feature of each vehicle, the vehicle identification feature and the vehicle passing time.

[0016] This application first segments the vehicle traffic image according to the deep learning-based image segmentation method to obtain vehicle color features, vehicle model features and vehicle damage features, and then performs normalization, splicing and standardization operations in succession to obtain a feature set of passing vehicles, which can facilitate the subsequent calculation of feature differences and improve the efficiency and accuracy of feature difference calculation.

[0017] In certain embodiments of the present application, the preset feature difference scoring method obtains a first toll evasion vehicle feature set based on the transit vehicle feature set and the road network vehicle feature set, specifically including:

[0018] Selecting each vehicle in the transit vehicle feature set as the current first vehicle in sequence, and obtaining an audit result of the current first vehicle according to a preset vehicle fee evasion audit method, until all vehicles in the transit vehicle feature set are selected, and obtaining a first fee evasion vehicle feature set according to the audit result;

[0019] The vehicle fee evasion and fee audit method is specifically as follows:

[0020] Calculating the feature difference scores between the current first vehicle and each vehicle in the road network vehicle feature set based on a preset feature difference scoring method;

[0021] If any one of the multiple feature difference scores of the current first vehicle reaches a preset threshold, the license plate numbers of the current first vehicle and the current second vehicle are obtained based on a plurality of frames of vehicle traffic images of the current first vehicle and a plurality of frames of vehicle traffic images of the current second vehicle; wherein the current second vehicle is a vehicle in the road network vehicle feature set corresponding to the feature difference score that reaches the preset threshold;

[0022] If the license plate number of the current first vehicle is different from the license plate number of the current second vehicle, the vehicle registration information of the current first vehicle is obtained according to the vehicle identification feature of the current first vehicle, and the audit result of the current first vehicle is obtained according to the vehicle registration information.

[0023] The present application first selects the current first vehicle in the passing vehicle feature set, then obtains the feature difference score between the current first vehicle and each vehicle in the road network vehicle feature set, and when any one of the multiple feature difference scores reaches a preset threshold, obtains and compares the corresponding license plate numbers of the current first vehicle and the current second vehicle. It can first exclude two vehicles with similar features but are actually different, determine whether the current first vehicle and the current second vehicle are the same vehicle, and then obtain the vehicle registration information based on the comparison result of the license plate numbers, and then obtain the audit result, which can exclude vehicles with legal license plate changes, improve the recognition accuracy of vehicles that evade fees, and thus improve the accuracy of auditing vehicles that evade fees.

[0024] In certain embodiments of the present application, the feature difference scoring method is specifically:

[0025] Selecting each vehicle in the road network vehicle feature set in turn as a current scoring vehicle, and calculating a feature difference score between the current first vehicle and the current scoring vehicle according to a preset first scoring method, until all vehicles in the road network vehicle feature set have been selected;

[0026] The first scoring method is specifically:

[0027] Based on the similarity algorithm, multiple similarities between the current first vehicle and the current scoring vehicle are calculated; wherein the multiple similarities are based on vehicle color features, vehicle model features, vehicle damage features, and vehicle identification features of the current first vehicle and the current scoring vehicle;

[0028] A feature difference score between the current first vehicle and the current scoring vehicle is obtained according to the multiple similarities.

[0029] The present application first selects the currently scored vehicle in the road network vehicle feature set, and then based on the similarity algorithm, calculates multiple similarities between the current first vehicle and the currently scored vehicle, including vehicle color features, vehicle model features, vehicle damage features and vehicle identification features, and determines the feature difference score based on the multiple similarities. This can improve the accuracy of the feature difference score, and further more accurately determine whether the current first vehicle and the currently scored vehicle are the same vehicle, thereby improving the recognition accuracy of similar vehicles.

[0030] In certain embodiments of the present application, the first toll evasion vehicle feature set is screened based on a preset road network model to obtain a second toll evasion vehicle feature set, specifically including:

[0031] Selecting each vehicle in the first vehicle feature set as a first identification vehicle in sequence, and obtaining an identification result of the first identification vehicle according to a preset vehicle identification method for evading fees, until all vehicles in the first vehicle feature set for evading fees have been selected;

[0032] The method for determining whether a vehicle has evaded or missed a fee is specifically as follows:

[0033] Acquire multiple toll station passing data corresponding to the first identified vehicle; wherein the toll station passing data includes toll station location data, vehicle passing time and vehicle toll;

[0034] Based on a preset road network model, according to the toll station location data and the vehicle passing time, a first driving path of the first identified vehicle is obtained;

[0035] Based on the road network model and the preset path algorithm, and according to the toll station location data, a first theoretical path and a plurality of first theoretical fees for a first identified vehicle are obtained;

[0036] Obtaining a first discrimination result of a first vehicle according to the first driving path and the first theoretical path;

[0037] Obtaining a second discrimination result of the first discrimination vehicle according to the vehicle toll and the plurality of first theoretical fees;

[0038] Obtaining a third discrimination result of the first discrimination vehicle according to the vehicle passing time;

[0039] According to the first discrimination result, the second discrimination result and the third discrimination result, a discrimination result of toll evasion of the first discrimination vehicle is obtained.

[0040] This application first selects a first identification vehicle, then obtains the traffic data of multiple toll stations corresponding to the first identification vehicle, and based on the road network model and path algorithm, obtains the first driving path, the first theoretical path and multiple first theoretical fees, and then obtains the first identification result, the second identification result and the third identification result. This multi-angle identification can improve the accuracy of the final toll evasion identification, and thus improve the accuracy of the audit of vehicle toll evasion.

[0041] In certain embodiments of the present application, the calculating, based on the vehicle traffic data, the highway toll payable for each vehicle in the second toll evasion vehicle feature set specifically includes:

[0042] Selecting each vehicle in the second toll evasion vehicle feature set as the current toll-charging vehicle in sequence, and obtaining the toll payable by the current toll-charging vehicle according to a preset toll evasion charging method, until all vehicles in the second toll evasion vehicle feature set have been selected;

[0043] The method for charging evasion fees is as follows:

[0044] Determine the entry and exit data of the toll station corresponding to the current billing vehicle according to the multiple vehicle travel times of the current billing vehicle;

[0045] If both the entry data and the exit data are not empty, the highway toll payable for the current billing vehicle is obtained according to the entry data and the exit data;

[0046] If either the entry data or the exit data is empty, the second theoretical path of the current billing vehicle is obtained based on the vehicle passage data of the current billing vehicle, and the compensable highway toll of the current billing vehicle is obtained based on the second theoretical path.

[0047] This application first selects the current billing vehicle, and then determines the corresponding toll station's entry and exit data based on the multiple vehicle travel times of the current billing vehicle, and calculates the highway toll that the current billing vehicle should pay based on the null value of the entry and exit data. This can improve the accuracy of calculating the highway toll that should be paid for vehicles that evade fees, and thereby improve the accuracy of auditing vehicles that evade fees.

[0048] According to a second aspect of the implementation of the present application, there is provided a vehicle fee evasion audit device, comprising a vehicle feature extraction module, a feature set segmentation module, a first fee evasion set determination module, a second fee evasion set determination module and a vehicle fee supplement calculation module;

[0049] The vehicle feature extraction module is used to obtain vehicle traffic data of multiple toll stations and multiple highway sections, and perform feature extraction on the vehicle traffic data to obtain a traffic vehicle feature set; wherein the multiple highway sections are located between the multiple toll stations; the vehicle traffic data includes vehicle traffic time, vehicle identification features and a plurality of frames of vehicle traffic images;

[0050] The feature set segmentation module is used to segment the passing vehicle feature set based on the preset passing feature markers to obtain the passing vehicle feature set and the road network vehicle feature set;

[0051] The first fee evasion set determination module is used to obtain a first fee evasion vehicle feature set based on the transit vehicle feature set and the road network vehicle feature set based on a preset feature difference scoring method;

[0052] The second fee evasion set determination module is used to screen the first fee evasion vehicle feature set based on a preset road network model to obtain a second fee evasion vehicle feature set;

[0053] The vehicle fee calculation module is used to calculate the highway fee that should be paid for each vehicle in the second toll evasion vehicle feature set based on the vehicle travel data, so as to conduct vehicle toll evasion audit based on the toll that should be paid.

[0054] In certain embodiments of the present application, the vehicle feature extraction module includes an image segmentation submodule, an image normalization submodule, a feature specification submodule, and a feature acquisition submodule;

[0055] The image segmentation submodule is used to segment a plurality of frames of vehicle traffic images of each vehicle in the vehicle traffic data according to an image segmentation method based on deep learning, so as to obtain vehicle color features, vehicle model features and vehicle damage features of each vehicle;

[0056] The image normalization submodule is used to normalize and splice the vehicle color feature, vehicle model feature, and vehicle damage feature of each vehicle to obtain the first comprehensive feature of each vehicle;

[0057] The feature standardization submodule is used to standardize the first comprehensive feature of each vehicle based on a preset standard to obtain the second comprehensive feature of each vehicle;

[0058] The feature acquisition submodule is used to obtain a passing vehicle feature set based on the second comprehensive feature of each vehicle, the vehicle identification feature and the vehicle passing time.

[0059] In certain embodiments of the present application, the first evasion fee set determination module includes a first evasion fee set determination submodule;

[0060] The first fee evasion set determination submodule is used to sequentially select each vehicle in the transit vehicle feature set as the current first vehicle, and obtain the audit result of the current first vehicle according to a preset vehicle fee evasion audit method, until all vehicles in the transit vehicle feature set are selected, and obtain the first fee evasion vehicle feature set according to the audit result;

[0061] The vehicle fee evasion and fee audit method is specifically as follows:

[0062] Calculating the feature difference scores between the current first vehicle and each vehicle in the road network vehicle feature set based on a preset feature difference scoring method;

[0063] If any one of the multiple feature difference scores of the current first vehicle reaches a preset threshold, the license plate numbers of the current first vehicle and the current second vehicle are obtained based on a plurality of frames of vehicle traffic images of the current first vehicle and a plurality of frames of vehicle traffic images of the current second vehicle; wherein the current second vehicle is a vehicle in the road network vehicle feature set corresponding to the feature difference score that reaches the preset threshold;

[0064] If the license plate number of the current first vehicle is different from the license plate number of the current second vehicle, the vehicle registration information of the current first vehicle is obtained according to the vehicle identification feature of the current first vehicle, and the audit result of the current first vehicle is obtained according to the vehicle registration information.

[0065] In certain embodiments of the present application, the feature difference scoring method is specifically:

[0066] Selecting each vehicle in the road network vehicle feature set in turn as a current scoring vehicle, and calculating a feature difference score between the current first vehicle and the current scoring vehicle according to a preset first scoring method, until all vehicles in the road network vehicle feature set have been selected;

[0067] The first scoring method is specifically:

[0068] Based on the similarity algorithm, multiple similarities between the current first vehicle and the current scoring vehicle are calculated; wherein the multiple similarities are based on vehicle color features, vehicle model features, vehicle damage features, and vehicle identification features of the current first vehicle and the current scoring vehicle;

[0069] A feature difference score between the current first vehicle and the current scoring vehicle is obtained according to the multiple similarities.

[0070] In certain embodiments of the present application, the second evasion fee set determination module includes a second evasion fee set determination submodule;

[0071] The second fee evasion set determination submodule is used to sequentially select each vehicle in the first fee evasion vehicle feature set as a first discrimination vehicle, and obtain a fee evasion discrimination result of the first discrimination vehicle according to a preset vehicle fee evasion discrimination method, until all vehicles in the first fee evasion vehicle feature set are selected;

[0072] The method for determining whether a vehicle has evaded or missed a fee is specifically as follows:

[0073] Acquire multiple toll station passing data corresponding to the first identified vehicle; wherein the toll station passing data includes toll station location data, vehicle passing time and vehicle toll;

[0074] Based on a preset road network model, according to the toll station location data and the vehicle passing time, a first driving path of the first identified vehicle is obtained;

[0075] Based on the road network model and the preset path algorithm, and according to the toll station location data, a first theoretical path and a plurality of first theoretical fees for a first identified vehicle are obtained;

[0076] Obtaining a first discrimination result of a first vehicle according to the first driving path and the first theoretical path;

[0077] Obtaining a second discrimination result of the first discrimination vehicle according to the vehicle toll and the plurality of first theoretical fees;

[0078] Obtaining a third discrimination result of the first discrimination vehicle according to the vehicle passing time;

[0079] According to the first discrimination result, the second discrimination result and the third discrimination result, a discrimination result of toll evasion of the first discrimination vehicle is obtained.

[0080] In certain embodiments of the present application, the vehicle fee calculation module includes a vehicle fee calculation submodule;

[0081] The vehicle toll calculation submodule is used to sequentially select each vehicle in the second toll evasion vehicle feature set as the current toll-charging vehicle, and obtain the toll to be paid for the current toll-charging vehicle according to a preset toll evasion charging method, until all vehicles in the second toll evasion vehicle feature set are selected;

[0082] The method for charging evasion fees is as follows:

[0083] Determine the entry and exit data of the toll station corresponding to the current billing vehicle according to the multiple vehicle travel times of the current billing vehicle;

[0084] If both the entry data and the exit data are not empty, the highway toll payable for the current billing vehicle is obtained according to the entry data and the exit data;

[0085] If either the entry data or the exit data is empty, the second theoretical path of the current billing vehicle is obtained based on the vehicle passage data of the current billing vehicle, and the compensable highway toll of the current billing vehicle is obtained based on the second theoretical path.

[0086] The present application first extracts features from vehicle traffic data to obtain a passing vehicle feature set, then divides the passing vehicle feature set to obtain a passing vehicle feature set and a road network vehicle feature set, and then obtains a first toll evasion vehicle feature set based on a feature difference scoring method. By comparing the feature differences between vehicles passing through toll stations and vehicles passing through expressway sections, the recognition accuracy of similar vehicles can be improved and the probability of misjudgment can be reduced. The first toll evasion vehicle feature set is then screened based on a road network model to obtain a second toll evasion vehicle feature set. Based on the road network model, some normal passing vehicles that are not toll evasion can be screened out, thereby improving the accuracy of vehicle toll evasion audits when calculating the tolls that should be paid for each vehicle in the second toll evasion vehicle feature set for vehicle toll evasion audits. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 : A flow chart of a method for auditing vehicle fee evasion shown in certain embodiments of the present application;

[0088] Figure 2 : A module structure diagram of a vehicle fee evasion and auditing device shown in certain embodiments of the present application. DETAILED DESCRIPTION

[0089] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below in conjunction with the accompanying drawings are exemplary and are only used to explain some embodiments of the present application, and should not be construed as limitations on the embodiments of the present application. Based on the embodiments shown in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0090] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, unless otherwise clearly and specifically defined, "multiple" and "several" mean two or more.

[0091] At present, there are two main difficulties in auditing vehicles that evade fees: (1) It is difficult to identify similar vehicles, which can easily lead to misjudgment, thereby reducing the accuracy of the audit; (2) In the process of inter-provincial travel, it is difficult to exclude vehicles with legally changed license plates, which increases the difficulty of correctly identifying vehicles that evade fees by using false license plates, thereby increasing the difficulty of auditing and reducing the accuracy of auditing. Therefore, how to improve the accuracy of vehicle fee evasion audits is still a problem that needs to be solved urgently in the existing technology.

[0092] Based on the above technical background, please refer to Figure 1 The embodiment of the present application provides a method for checking vehicle fee evasion, including steps S101 to S105, each of which is as follows:

[0093] Step S101: Acquire vehicle traffic data of multiple toll stations and multiple expressway sections, and perform feature extraction on the vehicle traffic data to obtain a feature set of passing vehicles; wherein the multiple expressway sections are located between the multiple toll stations; the vehicle traffic data includes vehicle traffic time, vehicle identification features and several frames of vehicle traffic images.

[0094] In certain embodiments of the present application, extracting features from the vehicle traffic data to obtain a traffic vehicle feature set specifically includes:

[0095] According to the deep learning-based image segmentation method, a plurality of frames of vehicle traffic images of each vehicle in the vehicle traffic data are segmented to obtain vehicle color features, vehicle model features, and vehicle damage features of each vehicle;

[0096] Normalize and concatenate the vehicle color feature, vehicle model feature, and vehicle damage feature of each vehicle to obtain the first comprehensive feature of each vehicle;

[0097] Based on a preset standard, the first comprehensive feature of each vehicle is normalized to obtain a second comprehensive feature of each vehicle;

[0098] A passing vehicle feature set is obtained according to the second comprehensive feature of each vehicle, the vehicle identification feature and the vehicle passing time.

[0099] In certain embodiments of the present application, the preferred segmentation model of the deep learning-based image segmentation method is a convolutional neural network, which includes but is not limited to R-CNN, Fast R-CNN and Faster R-CNN.

[0100] In certain embodiments of the present application, a preferred implementation of the vehicle color feature is a vector composed of the mean and variance of the RGB three channels of the vehicle body; a preferred implementation of the vehicle model feature is a vector composed of the aspect ratio of the vehicle body contour and the height ratio between the front of the vehicle and the body; a preferred implementation of the vehicle damage feature is a vector composed of the position coordinates of the vehicle body scratches and vehicle body dents obtained based on the recognition algorithm.

[0101] This application first segments the vehicle traffic image according to the deep learning-based image segmentation method to obtain vehicle color features, vehicle model features and vehicle damage features, and then performs normalization, splicing and standardization operations in succession to obtain a feature set of passing vehicles, which can facilitate the subsequent calculation of feature differences and improve the efficiency and accuracy of feature difference calculation.

[0102] Step S102: based on preset traffic feature markers, the traffic vehicle feature set is segmented to obtain a passing vehicle feature set and a road network vehicle feature set.

[0103] In certain embodiments of the present application, the traffic feature mark is a vehicle traffic section type, including a toll booth and a highway section.

[0104] Step S103: Based on a preset feature difference scoring method, a first toll evasion vehicle feature set is obtained according to the transit vehicle feature set and the road network vehicle feature set.

[0105] In certain embodiments of the present application, the preset feature difference scoring method obtains a first toll evasion vehicle feature set based on the transit vehicle feature set and the road network vehicle feature set, specifically including:

[0106] Selecting each vehicle in the transit vehicle feature set as the current first vehicle in sequence, and obtaining an audit result of the current first vehicle according to a preset vehicle fee evasion audit method, until all vehicles in the transit vehicle feature set are selected, and obtaining a first fee evasion vehicle feature set according to the audit result;

[0107] The vehicle fee evasion and fee audit method is specifically as follows:

[0108] Calculating the feature difference scores between the current first vehicle and each vehicle in the road network vehicle feature set based on a preset feature difference scoring method;

[0109] If any one of the multiple feature difference scores of the current first vehicle reaches a preset threshold, the license plate numbers of the current first vehicle and the current second vehicle are obtained based on a plurality of frames of vehicle traffic images of the current first vehicle and a plurality of frames of vehicle traffic images of the current second vehicle; wherein the current second vehicle is a vehicle in the road network vehicle feature set corresponding to the feature difference score that reaches the preset threshold;

[0110] If the license plate number of the current first vehicle is different from the license plate number of the current second vehicle, the vehicle registration information of the current first vehicle is obtained according to the vehicle identification feature of the current first vehicle, and the audit result of the current first vehicle is obtained according to the vehicle registration information.

[0111] In certain embodiments of the present application, the audit result of the current first vehicle is obtained according to the vehicle registration information, specifically:

[0112] If there is no license plate change record in the vehicle registration information, the current audit result of the first vehicle is "toll evasion vehicle".

[0113] The present application first selects the current first vehicle in the passing vehicle feature set, then obtains the feature difference score between the current first vehicle and each vehicle in the road network vehicle feature set, and when any one of the multiple feature difference scores reaches a preset threshold, obtains and compares the corresponding license plate numbers of the current first vehicle and the current second vehicle. It can first exclude two vehicles with similar features but are actually different, determine whether the current first vehicle and the current second vehicle are the same vehicle, and then obtain the vehicle registration information based on the comparison result of the license plate numbers, and then obtain the audit result, which can exclude vehicles with legal license plate changes, improve the recognition accuracy of vehicles that evade fees, and thus improve the accuracy of auditing vehicles that evade fees.

[0114] In certain embodiments of the present application, the feature difference scoring method is specifically:

[0115] Selecting each vehicle in the road network vehicle feature set in turn as a current scoring vehicle, and calculating a feature difference score between the current first vehicle and the current scoring vehicle according to a preset first scoring method, until all vehicles in the road network vehicle feature set have been selected;

[0116] The first scoring method is specifically:

[0117] Based on the similarity algorithm, multiple similarities between the current first vehicle and the current scoring vehicle are calculated; wherein the multiple similarities are based on vehicle color features, vehicle model features, vehicle damage features, and vehicle identification features of the current first vehicle and the current scoring vehicle;

[0118] A feature difference score between the current first vehicle and the current scoring vehicle is obtained according to the multiple similarities.

[0119] In certain embodiments of the present application, preferred implementations of the similarity algorithm include but are not limited to a cosine similarity algorithm, a PSNR algorithm, a structural similarity (SSIM) algorithm, and a mean hashing algorithm.

[0120] In certain embodiments of the present application, the feature difference score between the current first vehicle and the current scoring vehicle is obtained based on the multiple similarities, specifically:

[0121] A weighted sum of the multiple similarities under preset weights is used as a feature difference score between the current first vehicle and the current scoring vehicle.

[0122] In certain embodiments of the present application, the preferred implementation scheme of the preset weights is as follows: the weights of the similarities corresponding to vehicle color features, vehicle model features, vehicle damage features and vehicle identification features are 0.3, 0.4, 0.2 and 0.1 respectively.

[0123] The present application first selects the currently scored vehicle in the road network vehicle feature set, and then based on the similarity algorithm, calculates multiple similarities between the current first vehicle and the currently scored vehicle, including vehicle color features, vehicle model features, vehicle damage features and vehicle identification features, and determines the feature difference score based on the multiple similarities. This can improve the accuracy of the feature difference score, and further more accurately determine whether the current first vehicle and the currently scored vehicle are the same vehicle, thereby improving the recognition accuracy of similar vehicles.

[0124] Step S104: Based on a preset road network model, the first toll evasion vehicle feature set is screened to obtain a second toll evasion vehicle feature set.

[0125] In certain embodiments of the present application, the first toll evasion vehicle feature set is screened based on a preset road network model to obtain a second toll evasion vehicle feature set, specifically including:

[0126] Selecting each vehicle in the first vehicle feature set as a first identification vehicle in sequence, and obtaining an identification result of the first identification vehicle according to a preset vehicle identification method for evading fees, until all vehicles in the first vehicle feature set for evading fees have been selected;

[0127] The method for determining whether a vehicle has evaded or missed a fee is specifically as follows:

[0128] Acquire multiple toll station passing data corresponding to the first identified vehicle; wherein the toll station passing data includes toll station location data, vehicle passing time and vehicle toll;

[0129] Based on a preset road network model, according to the toll station location data and the vehicle passing time, a first driving path of the first identified vehicle is obtained;

[0130] Based on the road network model and the preset path algorithm, and according to the toll station location data, a first theoretical path and a plurality of first theoretical fees for a first identified vehicle are obtained;

[0131] Obtaining a first discrimination result of a first vehicle according to the first driving path and the first theoretical path;

[0132] Obtaining a second discrimination result of the first discrimination vehicle according to the vehicle toll and the plurality of first theoretical fees;

[0133] Obtaining a third discrimination result of the first discrimination vehicle according to the vehicle passing time;

[0134] According to the first discrimination result, the second discrimination result and the third discrimination result, a discrimination result of toll evasion of the first discrimination vehicle is obtained.

[0135] In certain embodiments of the present application, the first driving path of the first identified vehicle is obtained based on the preset road network model according to the toll station location data and the vehicle passing time, specifically:

[0136] According to the toll station location data, a plurality of first passing toll stations corresponding to the first identified vehicle are obtained;

[0137] Based on a preset road network model, the plurality of first toll stations are connected in the order of the vehicle's passing time to obtain a first driving path of the first identified vehicle.

[0138] In certain embodiments of the present application, based on the road network model and the preset path algorithm, the first theoretical path and multiple first theoretical fees of the first identified vehicle are obtained according to the toll station location data, specifically:

[0139] According to the toll station location data, a plurality of second toll stations corresponding to the first identified vehicle are obtained;

[0140] Based on the road network model and the preset path algorithm, the plurality of second passing toll stations are connected to obtain a first theoretical path of the first identified vehicle;

[0141] A plurality of first theoretical charges are obtained according to the plurality of second toll stations and the first theoretical path.

[0142] In certain embodiments of the present application, the preset path algorithm includes but is not limited to an A* algorithm, a Dijkstra algorithm, and a simulated annealing algorithm.

[0143] In certain embodiments of the present application, the first identification result of the first vehicle identification is obtained according to the first driving path and the first theoretical path, specifically:

[0144] Calculating the path similarity between the first driving path and the first theoretical path according to a preset path similarity algorithm;

[0145] If the path similarity is lower than a preset path similarity threshold, the first identification result of the first identification vehicle is "toll evasion vehicle".

[0146] In certain embodiments of the present application, the path similarity algorithm includes but is not limited to a dynamic time normalization algorithm, a path similarity algorithm based on edit distance, and a path similarity algorithm based on the longest common subsequence.

[0147] In certain embodiments of the present application, the second discrimination result of the first discrimination vehicle is obtained according to the vehicle toll and the plurality of first theoretical fees, specifically:

[0148] If the ratio of the sum of the plurality of first theoretical fees to the sum of the plurality of vehicle tolls does not fall within a preset fee ratio interval, the second determination result of the first determination of the vehicle is "abnormal vehicle".

[0149] In certain embodiments of the present application, the third discrimination result of the first discrimination vehicle is obtained according to the vehicle passing time, specifically:

[0150] According to the toll station location data, a plurality of third toll stations passed by the first identified vehicle are obtained;

[0151] Obtaining a plurality of preset travel times between the plurality of third toll stations;

[0152] If the ratio of the transit time of multiple vehicles to the preset travel time of the corresponding third toll station is greater than the preset travel time ratio threshold, the third judgment result of the first judgment vehicle is "toll evasion vehicle".

[0153] In certain embodiments of the present application, the first determination result of the toll evasion determination of the vehicle is obtained according to the first determination result, the second determination result and the third determination result, specifically:

[0154] If any one of the first judgment result and the third judgment result does not meet the preset judgment condition, and the second judgment result does not meet the preset judgment condition, then the first judgment result of the vehicle's toll evasion is "toll evasion".

[0155] This application first selects a first identification vehicle, then obtains the traffic data of multiple toll stations corresponding to the first identification vehicle, and based on the road network model and path algorithm, obtains the first driving path, the first theoretical path and multiple first theoretical fees, and then obtains the first identification result, the second identification result and the third identification result. This multi-angle identification can improve the accuracy of the final toll evasion identification, and thus improve the accuracy of the audit of vehicle toll evasion.

[0156] Step S105: Calculate the highway toll payable for each vehicle in the second highway toll evasion vehicle feature set based on the vehicle traffic data, so as to conduct a vehicle toll evasion audit based on the highway toll payable.

[0157] In certain embodiments of the present application, the calculating, based on the vehicle traffic data, the highway toll payable for each vehicle in the second toll evasion vehicle feature set specifically includes:

[0158] Selecting each vehicle in the second toll evasion vehicle feature set as the current toll-charging vehicle in sequence, and obtaining the toll payable by the current toll-charging vehicle according to a preset toll evasion charging method, until all vehicles in the second toll evasion vehicle feature set have been selected;

[0159] The method for charging evasion fees is as follows:

[0160] Determine the entry and exit data of the toll station corresponding to the current billing vehicle according to the multiple vehicle travel times of the current billing vehicle;

[0161] If both the entry data and the exit data are not empty, the highway toll payable for the current billing vehicle is obtained according to the entry data and the exit data;

[0162] If either the entry data or the exit data is empty, the second theoretical path of the current billing vehicle is obtained based on the vehicle passage data of the current billing vehicle, and the compensable highway toll of the current billing vehicle is obtained based on the second theoretical path.

[0163] In certain embodiments of the present application, the highway toll payable for the current charging vehicle is obtained based on the entry data and the exit data, specifically:

[0164] According to the exit data, the actual highway toll paid by the current charging vehicle is obtained;

[0165] Obtain the highway toll payable by the current charging vehicle according to the entry data and the exit data;

[0166] The toll payable for the current vehicle is obtained based on the actually paid toll and the payable toll.

[0167] In certain embodiments of the present application, the second theoretical path of the current toll-charging vehicle is obtained according to the vehicle passage data of the current toll-charging vehicle, specifically:

[0168] According to the vehicle passage data of the current toll-charging vehicle, a plurality of fourth toll stations corresponding to the current toll-charging vehicle are obtained;

[0169] Based on the road network model and the vehicle traffic data of the current billing vehicle, the multiple fourth toll stations are connected to obtain the second theoretical path of the current billing vehicle.

[0170] In certain implementations of the present application, the highway toll payable by the current charging vehicle is obtained according to the second theoretical path, specifically:

[0171] According to the second theoretical path, obtaining the highway toll payable by the current charging vehicle;

[0172] The payable highway toll is used as the highway toll payable for the current billing vehicle.

[0173] This application first selects the current billing vehicle, and then determines the corresponding toll station's entry and exit data based on the multiple vehicle travel times of the current billing vehicle, and calculates the highway toll that the current billing vehicle should pay based on the null value of the entry and exit data. This can improve the accuracy of calculating the highway toll that should be paid for vehicles that evade fees, and thereby improve the accuracy of auditing vehicles that evade fees.

[0174] Compared with the prior art, the present application first extracts features from vehicle traffic data to obtain a feature set of passing vehicles, and then divides the feature set of passing vehicles to obtain a feature set of passing vehicles and a feature set of road network vehicles, and then obtains a first feature set of toll evading vehicles based on a feature difference scoring method. By comparing the feature differences between vehicles passing through toll stations and vehicles passing through expressway sections, the accuracy of identifying similar vehicles can be improved and the probability of misjudgment can be reduced. The first feature set of toll evading vehicles can be screened based on a road network model to obtain a second feature set of toll evading vehicles. Some normal passing vehicles that are not toll evading vehicles can be screened out based on the road network model, and the accuracy of vehicle toll evasion audit can be improved when calculating the tolls to be paid for each vehicle in the second feature set of toll evading vehicles for vehicle toll evasion audit.

[0175] Corresponding to the previous method, see Figure 2 The embodiment of the present application provides a vehicle fee evasion audit device, including a vehicle feature extraction module 210, a feature set segmentation module 220, a first fee evasion set determination module 230, a second fee evasion set determination module 240 and a vehicle fee supplement calculation module 250;

[0176] The vehicle feature extraction module 210 is used to obtain vehicle traffic data of multiple toll stations and multiple highway sections, and perform feature extraction on the vehicle traffic data to obtain a traffic vehicle feature set; wherein the multiple highway sections are located between the multiple toll stations; the vehicle traffic data includes vehicle traffic time, vehicle identification features and a plurality of frames of vehicle traffic images;

[0177] The feature set segmentation module 220 is used to segment the passing vehicle feature set based on the preset passing feature markers to obtain the passing vehicle feature set and the road network vehicle feature set;

[0178] The first toll evasion set determination module 230 is used to obtain a first toll evasion vehicle feature set based on the transit vehicle feature set and the road network vehicle feature set based on a preset feature difference scoring method;

[0179] The second fee evasion set determination module 240 is used to screen the first fee evasion vehicle feature set based on a preset road network model to obtain a second fee evasion vehicle feature set;

[0180] The vehicle fee calculation module 250 is used to calculate the highway fee that should be paid for each vehicle in the second toll evasion vehicle feature set based on the vehicle travel data, so as to conduct vehicle toll evasion audit based on the toll that should be paid.

[0181] In certain embodiments of the present application, the vehicle feature extraction module 210 includes an image segmentation submodule 211, an image normalization submodule 212, a feature specification submodule 213, and a feature acquisition submodule 214;

[0182] The image segmentation submodule 211 is used to segment a plurality of frames of vehicle traffic images of each vehicle in the vehicle traffic data according to an image segmentation method based on deep learning, so as to obtain vehicle color features, vehicle model features and vehicle damage features of each vehicle;

[0183] The image normalization submodule 212 is used to normalize and splice the vehicle color feature, vehicle model feature, and vehicle damage feature of each vehicle to obtain a first comprehensive feature of each vehicle;

[0184] The feature standardization submodule 213 is used to standardize the first comprehensive feature of each vehicle based on a preset standard to obtain the second comprehensive feature of each vehicle;

[0185] The feature acquisition submodule 214 is used to obtain a passing vehicle feature set according to the second comprehensive feature of each vehicle, the vehicle identification feature and the vehicle passing time.

[0186] In certain embodiments of the present application, the first evasion fee set determination module 230 includes a first evasion fee set determination submodule 231;

[0187] The first fee evasion set determination submodule 231 is used to sequentially select each vehicle in the transit vehicle feature set as the current first vehicle, and obtain the audit result of the current first vehicle according to a preset vehicle fee evasion audit method, until all vehicles in the transit vehicle feature set are selected, and obtain the first fee evasion vehicle feature set according to the audit result;

[0188] The vehicle fee evasion and fee audit method is specifically as follows:

[0189] Calculating the feature difference scores between the current first vehicle and each vehicle in the road network vehicle feature set based on a preset feature difference scoring method;

[0190] If any one of the multiple feature difference scores of the current first vehicle reaches a preset threshold, the license plate numbers of the current first vehicle and the current second vehicle are obtained based on a plurality of frames of vehicle traffic images of the current first vehicle and a plurality of frames of vehicle traffic images of the current second vehicle; wherein the current second vehicle is a vehicle in the road network vehicle feature set corresponding to the feature difference score that reaches the preset threshold;

[0191] If the license plate number of the current first vehicle is different from the license plate number of the current second vehicle, the vehicle registration information of the current first vehicle is obtained according to the vehicle identification feature of the current first vehicle, and the audit result of the current first vehicle is obtained according to the vehicle registration information.

[0192] In certain embodiments of the present application, the feature difference scoring method is specifically:

[0193] Selecting each vehicle in the road network vehicle feature set in turn as a current scoring vehicle, and calculating a feature difference score between the current first vehicle and the current scoring vehicle according to a preset first scoring method, until all vehicles in the road network vehicle feature set have been selected;

[0194] The first scoring method is specifically:

[0195] Based on the similarity algorithm, multiple similarities between the current first vehicle and the current scoring vehicle are calculated; wherein the multiple similarities are based on vehicle color features, vehicle model features, vehicle damage features, and vehicle identification features of the current first vehicle and the current scoring vehicle;

[0196] A feature difference score between the current first vehicle and the current scoring vehicle is obtained according to the multiple similarities.

[0197] In certain embodiments of the present application, the second evasion fee set determination module 240 includes a second evasion fee set determination submodule 241;

[0198] The second fee evasion set determination submodule 241 is used to sequentially select each vehicle in the first fee evasion vehicle feature set as a first discrimination vehicle, and obtain a fee evasion discrimination result of the first discrimination vehicle according to a preset vehicle fee evasion discrimination method, until all vehicles in the first fee evasion vehicle feature set are selected;

[0199] The method for determining whether a vehicle has evaded or missed a fee is specifically as follows:

[0200] Acquire multiple toll station passing data corresponding to the first identified vehicle; wherein the toll station passing data includes toll station location data, vehicle passing time and vehicle toll;

[0201] Based on a preset road network model, according to the toll station location data and the vehicle passing time, a first driving path of the first identified vehicle is obtained;

[0202] Based on the road network model and the preset path algorithm, and according to the toll station location data, a first theoretical path and a plurality of first theoretical fees for a first identified vehicle are obtained;

[0203] Obtaining a first discrimination result of a first vehicle according to the first driving path and the first theoretical path;

[0204] Obtaining a second discrimination result of the first discrimination vehicle according to the vehicle toll and the plurality of first theoretical fees;

[0205] Obtaining a third discrimination result of the first discrimination vehicle according to the vehicle passing time;

[0206] According to the first discrimination result, the second discrimination result and the third discrimination result, a discrimination result of toll evasion of the first discrimination vehicle is obtained.

[0207] In certain embodiments of the present application, the vehicle fee calculation module 250 includes a vehicle fee calculation submodule 251;

[0208] The vehicle toll calculation submodule 251 is used to sequentially select each vehicle in the second toll evasion vehicle feature set as the current toll-charging vehicle, and obtain the toll to be paid for the current toll-charging vehicle according to the preset toll evasion charging method, until all vehicles in the second toll evasion vehicle feature set are selected;

[0209] The method for charging evasion fees is as follows:

[0210] Determine the entry and exit data of the toll station corresponding to the current billing vehicle according to the multiple vehicle travel times of the current billing vehicle;

[0211] If both the entry data and the exit data are not empty, the highway toll payable for the current billing vehicle is obtained according to the entry data and the exit data;

[0212] If either the entry data or the exit data is empty, the second theoretical path of the current billing vehicle is obtained based on the vehicle passage data of the current billing vehicle, and the compensable highway toll of the current billing vehicle is obtained based on the second theoretical path.

[0213] Compared with the prior art, the present application first extracts features from vehicle traffic data to obtain a feature set of passing vehicles, and then divides the feature set of passing vehicles to obtain a feature set of passing vehicles and a feature set of road network vehicles, and then obtains a first feature set of toll evading vehicles based on a feature difference scoring method. By comparing the feature differences between vehicles passing through toll stations and vehicles passing through expressway sections, the accuracy of identifying similar vehicles can be improved and the probability of misjudgment can be reduced. The first feature set of toll evading vehicles can be screened based on a road network model to obtain a second feature set of toll evading vehicles. Some normal passing vehicles that are not toll evading vehicles can be screened out based on the road network model, and the accuracy of vehicle toll evasion audit can be improved when calculating the tolls to be paid for each vehicle in the second feature set of toll evading vehicles for vehicle toll evasion audit.

[0214] It should be understood that the device provided in the embodiment of the present application corresponds to the aforementioned method, and a vehicle fee evasion and fee verification device provided in the embodiment of the present application can implement a vehicle fee evasion and fee verification method provided in any embodiment of the present application.

[0215] Adaptively, the embodiments of the present application also provide a computer device and a computer-readable storage medium.

[0216] The computer device comprises: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor;

[0217] Among them, when the processor executes the computer program, a vehicle fee evasion and fee verification method of the present application is implemented.

[0218] The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute a vehicle fee evasion and fee verification method of the present application.

[0219] The above are some implementation methods of the present application, and the purpose, technical scheme and beneficial effects of the present application are further described in detail. It should be clear that the above-mentioned partial implementation methods of the present application cannot be understood as limiting the present application. It is particularly pointed out that for those skilled in the art, any changes, modifications, equivalent substitutions and variations made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A vehicle fee evasion and fee audit method, characterized in that: include: Acquire vehicle traffic data of multiple toll stations and multiple highway sections, and perform feature extraction on the vehicle traffic data to obtain a feature set of passing vehicles; wherein the multiple highway sections are located between the multiple toll stations; the vehicle traffic data includes vehicle traffic time, vehicle identification features, and a plurality of frames of vehicle traffic images; Based on the preset passing feature markers, the passing vehicle feature set is segmented to obtain a passing vehicle feature set and a road network vehicle feature set; Based on a preset feature difference scoring method, a first toll evasion vehicle feature set is obtained according to the transit vehicle feature set and the road network vehicle feature set; Based on a preset road network model, the first toll evasion vehicle feature set is screened to obtain a second toll evasion vehicle feature set; The highway toll payable of each vehicle in the second toll evasion vehicle feature set is calculated based on the vehicle traffic data, so as to conduct a vehicle toll evasion audit based on the toll payable toll.

2. A vehicle fee evasion and fee audit method according to claim 1, characterized in that: The feature extraction of the vehicle traffic data to obtain a feature set of the passing vehicles specifically includes: According to the deep learning-based image segmentation method, a plurality of frames of vehicle traffic images of each vehicle in the vehicle traffic data are segmented to obtain vehicle color features, vehicle model features, and vehicle damage features of each vehicle; Normalize and concatenate the vehicle color feature, vehicle model feature, and vehicle damage feature of each vehicle to obtain the first comprehensive feature of each vehicle; Based on a preset standard, the first comprehensive feature of each vehicle is normalized to obtain a second comprehensive feature of each vehicle; A passing vehicle feature set is obtained according to the second comprehensive feature of each vehicle, the vehicle identification feature and the vehicle passing time.

3. A vehicle fee evasion and fee audit method according to claim 2, characterized in that: The preset feature difference scoring method is based on the passing vehicle feature set and the road network vehicle feature set to obtain the first toll evasion vehicle feature set, specifically including: Selecting each vehicle in the transit vehicle feature set as the current first vehicle in sequence, and obtaining an audit result of the current first vehicle according to a preset vehicle fee evasion audit method, until all vehicles in the transit vehicle feature set are selected, and obtaining a first fee evasion vehicle feature set according to the audit result; The vehicle fee evasion and fee audit method is specifically as follows: Calculating the feature difference scores between the current first vehicle and each vehicle in the road network vehicle feature set based on a preset feature difference scoring method; If any one of the multiple feature difference scores of the current first vehicle reaches a preset threshold, the license plate numbers of the current first vehicle and the current second vehicle are obtained based on a plurality of frames of vehicle traffic images of the current first vehicle and a plurality of frames of vehicle traffic images of the current second vehicle; wherein the current second vehicle is a vehicle in the road network vehicle feature set corresponding to the feature difference score that reaches the preset threshold; If the license plate number of the current first vehicle is different from the license plate number of the current second vehicle, the vehicle registration information of the current first vehicle is obtained according to the vehicle identification feature of the current first vehicle, and the audit result of the current first vehicle is obtained according to the vehicle registration information.

4. A vehicle fee evasion and fee audit method according to claim 3, characterized in that: The feature difference scoring method is specifically as follows: Selecting each vehicle in the road network vehicle feature set in turn as a current scoring vehicle, and calculating a feature difference score between the current first vehicle and the current scoring vehicle according to a preset first scoring method, until all vehicles in the road network vehicle feature set have been selected; The first scoring method is specifically: Based on the similarity algorithm, multiple similarities between the current first vehicle and the current scoring vehicle are calculated; wherein the multiple similarities are based on vehicle color features, vehicle model features, vehicle damage features, and vehicle identification features of the current first vehicle and the current scoring vehicle; A feature difference score between the current first vehicle and the current scoring vehicle is obtained according to the multiple similarities.

5. A vehicle fee evasion and fee audit method according to claim 1, characterized in that: The first toll evasion vehicle feature set is screened based on the preset road network model to obtain the second toll evasion vehicle feature set, which specifically includes: Selecting each vehicle in the first vehicle feature set for toll evasion in sequence as a first identification vehicle, and obtaining an toll evasion identification result of the first identification vehicle according to a preset vehicle toll evasion identification method, until all vehicles in the first vehicle feature set for toll evasion have been selected; The method for determining whether a vehicle has evaded or missed a fee is specifically as follows: Acquire multiple toll station passing data corresponding to the first identified vehicle; wherein the toll station passing data includes toll station location data, vehicle passing time and vehicle toll; Based on a preset road network model, according to the toll station location data and the vehicle passing time, a first driving path of the first identified vehicle is obtained; Based on the road network model and the preset path algorithm, and according to the toll station location data, a first theoretical path and a plurality of first theoretical fees for a first identified vehicle are obtained; Obtaining a first discrimination result of a first vehicle according to the first driving path and the first theoretical path; Obtaining a second discrimination result of the first discrimination vehicle according to the vehicle toll and the plurality of first theoretical fees; Obtaining a third discrimination result of the first discrimination vehicle according to the vehicle passing time; According to the first discrimination result, the second discrimination result and the third discrimination result, a discrimination result of toll evasion of the first discrimination vehicle is obtained.

6. A vehicle fee evasion and fee audit method according to claim 1, characterized in that: The calculating, based on the vehicle traffic data, the highway toll payable by each vehicle in the second toll evasion vehicle feature set specifically includes: Selecting each vehicle in the second toll evasion vehicle feature set as the current toll-charging vehicle in sequence, and obtaining the toll payable by the current toll-charging vehicle according to a preset toll evasion charging method, until all vehicles in the second toll evasion vehicle feature set have been selected; The method for charging evasion fees is as follows: Determine the entry and exit data of the toll station corresponding to the current billing vehicle according to the multiple vehicle travel times of the current billing vehicle; If both the entry data and the exit data are not empty, the highway toll payable for the current billing vehicle is obtained according to the entry data and the exit data; If either the entry data or the exit data is empty, the second theoretical path of the current billing vehicle is obtained based on the vehicle passage data of the current billing vehicle, and the compensable highway toll of the current billing vehicle is obtained based on the second theoretical path.

7. A vehicle fee evasion and fee auditing device, characterized in that: It includes a vehicle feature extraction module, a feature set segmentation module, a first evasion fee set determination module, a second evasion fee set determination module and a vehicle fee supplement calculation module; The vehicle feature extraction module is used to obtain vehicle traffic data of multiple toll stations and multiple highway sections, and perform feature extraction on the vehicle traffic data to obtain a traffic vehicle feature set; wherein the multiple highway sections are located between the multiple toll stations; the vehicle traffic data includes vehicle traffic time, vehicle identification features and a plurality of frames of vehicle traffic images; The feature set segmentation module is used to segment the passing vehicle feature set based on the preset passing feature markers to obtain the passing vehicle feature set and the road network vehicle feature set; The first fee evasion set determination module is used to obtain a first fee evasion vehicle feature set based on the transit vehicle feature set and the road network vehicle feature set based on a preset feature difference scoring method; The second fee evasion set determination module is used to screen the first fee evasion vehicle feature set based on a preset road network model to obtain a second fee evasion vehicle feature set; The vehicle fee calculation module is used to calculate the highway fee that should be paid for each vehicle in the second toll evasion vehicle feature set based on the vehicle travel data, so as to conduct vehicle toll evasion audit based on the toll that should be paid.

8. The vehicle fee evasion and fee auditing device according to claim 7 is characterized in that: The vehicle feature extraction module includes an image segmentation submodule, an image normalization submodule, a feature specification submodule and a feature acquisition submodule; The image segmentation submodule is used to segment a plurality of frames of vehicle traffic images of each vehicle in the vehicle traffic data according to an image segmentation method based on deep learning, so as to obtain vehicle color features, vehicle model features and vehicle damage features of each vehicle; The image normalization submodule is used to normalize and splice the vehicle color feature, vehicle model feature, and vehicle damage feature of each vehicle to obtain the first comprehensive feature of each vehicle; The feature standardization submodule is used to standardize the first comprehensive feature of each vehicle based on a preset standard to obtain the second comprehensive feature of each vehicle; The feature acquisition submodule is used to obtain a passing vehicle feature set based on the second comprehensive feature of each vehicle, the vehicle identification feature and the vehicle passing time.

9. A vehicle fee evasion and fee auditing device according to claim 8, characterized in that: The first fee evasion set determination module includes a first fee evasion set determination submodule; The first fee evasion set determination submodule is used to sequentially select each vehicle in the transit vehicle feature set as the current first vehicle, and obtain the audit result of the current first vehicle according to a preset vehicle fee evasion audit method, until all vehicles in the transit vehicle feature set are selected, and obtain the first fee evasion vehicle feature set according to the audit result; The vehicle fee evasion and fee audit method is specifically as follows: Calculating the feature difference scores between the current first vehicle and each vehicle in the road network vehicle feature set based on a preset feature difference scoring method; If any one of the multiple feature difference scores of the current first vehicle reaches a preset threshold, the license plate numbers of the current first vehicle and the current second vehicle are obtained based on a plurality of frames of vehicle traffic images of the current first vehicle and a plurality of frames of vehicle traffic images of the current second vehicle; wherein the current second vehicle is a vehicle in the road network vehicle feature set corresponding to the feature difference score that reaches the preset threshold; If the license plate number of the current first vehicle is different from the license plate number of the current second vehicle, the vehicle registration information of the current first vehicle is obtained according to the vehicle identification feature of the current first vehicle, and the audit result of the current first vehicle is obtained according to the vehicle registration information.

10. A vehicle fee evasion and fee auditing device according to claim 9, characterized in that: The feature difference scoring method is specifically as follows: Selecting each vehicle in the road network vehicle feature set in turn as a current scoring vehicle, and calculating a feature difference score between the current first vehicle and the current scoring vehicle according to a preset first scoring method, until all vehicles in the road network vehicle feature set have been selected; The first scoring method is specifically: Based on the similarity algorithm, multiple similarities between the current first vehicle and the current scoring vehicle are calculated; wherein the multiple similarities are based on vehicle color features, vehicle model features, vehicle damage features, and vehicle identification features of the current first vehicle and the current scoring vehicle; A feature difference score between the current first vehicle and the current scoring vehicle is obtained according to the multiple similarities.

11. The vehicle fee evasion and fee auditing device according to claim 7, characterized in that: The second fee evasion set determination module includes a second fee evasion set determination submodule; The second fee evasion set determination submodule is used to sequentially select each vehicle in the first fee evasion vehicle feature set as a first discrimination vehicle, and obtain a fee evasion discrimination result of the first discrimination vehicle according to a preset vehicle fee evasion discrimination method, until all vehicles in the first fee evasion vehicle feature set are selected; The method for determining whether a vehicle has evaded or missed a fee is specifically as follows: Acquire multiple toll station passing data corresponding to the first identified vehicle; wherein the toll station passing data includes toll station location data, vehicle passing time and vehicle toll; Based on a preset road network model, according to the toll station location data and the vehicle passing time, a first driving path of the first identified vehicle is obtained; Based on the road network model and the preset path algorithm, and according to the toll station location data, a first theoretical path and a plurality of first theoretical fees for a first identified vehicle are obtained; Obtaining a first discrimination result of a first vehicle according to the first driving path and the first theoretical path; Obtaining a second discrimination result of the first discrimination vehicle according to the vehicle toll and the plurality of first theoretical fees; Obtaining a third discrimination result of the first discrimination vehicle according to the vehicle passing time; According to the first discrimination result, the second discrimination result and the third discrimination result, a discrimination result of toll evasion of the first discrimination vehicle is obtained.

12. The vehicle fee evasion and fee auditing device according to claim 7, characterized in that: The vehicle fee calculation module includes a vehicle fee calculation submodule; The vehicle toll calculation submodule is used to sequentially select each vehicle in the second toll evasion vehicle feature set as the current toll-charging vehicle, and obtain the toll to be paid for the current toll-charging vehicle according to a preset toll evasion charging method, until all vehicles in the second toll evasion vehicle feature set are selected; The method for charging evasion fees is as follows: Determine the entry and exit data of the toll station corresponding to the current billing vehicle according to the multiple vehicle travel times of the current billing vehicle; If both the entry data and the exit data are not empty, the highway toll payable for the current billing vehicle is obtained according to the entry data and the exit data; If either the entry data or the exit data is empty, the second theoretical path of the current billing vehicle is obtained based on the vehicle passage data of the current billing vehicle, and the compensable highway toll of the current billing vehicle is obtained based on the second theoretical path.

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