Highway auditing system based on multi-dimensional big data
By acquiring data from highway toll stations and video data to calculate the risk coefficient of violations and determine toll evasion, the problem of low efficiency in manual auditing has been solved, and rapid and accurate monitoring of highway auditing has been achieved.
Patent Information
- Application Number
- CN202411780929.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-05
- Publication Date
- 2025-11-07
AI Technical Summary
The current highway inspection system mainly relies on manual methods, resulting in low inspection efficiency.
By acquiring data from highway toll stations and video data from gantry camera systems, the system calculates the risk coefficient of vehicle violations and determines whether toll evasion has occurred based on the risk coefficient and passage time, issuing early warning information.
It enables rapid identification and precise monitoring of vehicle violations, improves audit and management efficiency, and reduces losses.
Smart Images
Figure CN120913288A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of highway auditing, and in particular to a highway auditing system based on multi-dimensional big data. BACKGROUND
[0002] With the continuous growth of transportation demand, highways, as an important part of modern transportation networks, their safety, efficiency and management level are increasingly valued. The auditing system based on big data technology can collect and analyze data from different sources in real time, significantly improve the accuracy and efficiency of auditing, and ensure timely detection of potential problems; through big data analysis, the running state of the highway can be monitored in real time, timely warning and corresponding measures can be taken; the auditing system based on multi-dimensional big data can help managers better understand the use of resources and changes in demand through in-depth analysis of operating data, reduce operating costs, improve resource use efficiency, and improve management level, laying a foundation for the sustainable development of highways; the auditing system based on multi-dimensional big data can not only be used for daily management and operation, but also provides a scientific basis for policy making and evaluation. Through the analysis of a large amount of data, government departments can better understand the use of highways and its impact on the economy and the environment, providing data support and theoretical basis for formulating reasonable transportation policies, improving infrastructure, and promoting the development of green transportation. Therefore, the highway auditing system based on multi-dimensional big data has important significance.
[0003] At present, the existing highway auditing is usually audited by manual method, which is time-consuming and laborious, resulting in low efficiency of highway auditing. SUMMARY
[0004] The present disclosure provides a highway auditing system based on multi-dimensional big data.
[0005] According to a first aspect of the present disclosure, a highway auditing system based on multi-dimensional big data is provided. The system comprises:
[0006] an acquisition module for acquiring toll data of a target vehicle at any toll station of a highway, the toll data including a toll entry name, a toll exit name and a toll time; determining a toll path of the target vehicle according to the toll entry name and the toll exit name; and acquiring video data corresponding to each gantry camera collection system on the toll path;
[0007] a calculation module for calculating a violation risk coefficient of the target vehicle according to each of the video data;
[0008] a warning module for determining whether the target vehicle has a toll evasion behavior according to the violation risk coefficient and the toll time; and if so, issuing a warning information.
[0009] Further, the calculation of the violation risk coefficient of the target vehicle according to each video data comprises: obtaining the number of times of overspeeding of the target vehicle when passing through each gantry camera collection system according to each video data; and calculating the overspeeding violation risk coefficient of the target vehicle according to the number of times of overspeeding and the corresponding weight.
[0010] Further, the calculation of the violation risk coefficient of the target vehicle according to each video data further comprises: obtaining the number of times of illegal lane changing of the target vehicle when passing through the target area of each gantry camera collection system according to each video data; and calculating the lane changing violation risk coefficient of the target vehicle according to the number of times of illegal lane changing and the corresponding weight.
[0011] Further, the calculation of the violation risk coefficient of the target vehicle according to each video data further comprises: obtaining the number of times of illegal driving of the target vehicle when passing through the target area of each gantry camera collection system according to each video data; and calculating the driving violation risk coefficient of the target vehicle according to the number of times of illegal driving and the corresponding weight.
[0012] Further, the calculation formula of the violation risk coefficient of the target vehicle is:
[0013]
[0014] Wherein, W is the violation risk coefficient of the target vehicle, C is the overspeeding violation risk coefficient of the target vehicle, B is the lane changing violation risk coefficient of the target vehicle, and J is the driving violation risk coefficient of the target vehicle.
[0015] Further, the determination of whether the target vehicle has an evasion behavior according to the violation risk coefficient and the road passing time; if yes, a warning information is sent, comprising:
[0016] If 0≤W<W1, it is determined that the state of the target vehicle during the current road passing path is slight violation;
[0017] If W1≤W<W2, it is determined that the state of the target vehicle during the current road passing path is general violation;
[0018] If W2≤W<1, it is determined that the state of the target vehicle during the current road passing path is serious violation; 0<W1<W2<1, W1 is a first preset violation risk coefficient, and W2 is a second preset violation risk coefficient.
[0019] Further, the determination of whether the target vehicle has an evasion behavior according to the violation risk coefficient and the road passing time; if yes, a warning information is sent, comprising:
[0020] If the state of the target vehicle during the current overpass path is a serious violation, the overpass time is calculated according to the overpass path calculation theory;
[0021] According to the overpass time T of the target vehicle and the theoretical overpass time T', it is determined whether the target vehicle has an evasion behavior;
[0022] If T>T' *1.5, it is determined that the target vehicle has an evasion behavior; if T' *1.5
[0023] According to a second aspect of the present disclosure, a highway auditing method based on multi-dimensional big data is provided. The method comprises:
[0024] Obtaining the toll data of a target vehicle at any toll station of the highway, the toll data comprising a toll entry station name, a toll exit station name and an overpass time; determining the overpass path of the target vehicle according to the toll entry station name and the toll exit station name; and obtaining the video data corresponding to each gantry camera collection system on the overpass path;
[0025] According to each of the video data, the violation risk coefficient of the target vehicle is calculated;
[0026] According to the violation risk coefficient and the overpass time, it is determined whether the target vehicle has an evasion behavior; if yes, a warning information is sent.
[0027] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device comprises a memory and a processor, the memory having a computer program stored thereon, and the processor implementing the method when executing the program.
[0028] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, having a computer program stored thereon, the program being executed by a processor to implement the method.
[0029] Compared with the prior art, the beneficial effects of the present disclosure are that the present disclosure can quickly master the road information of the target vehicle, including the names and times of the entry and exit stations, by real-time acquisition of data of the highway toll station, providing accurate data support for subsequent analysis; according to the names of the toll entry and exit stations, the road path of the target vehicle can be clearly determined, ensuring the pertinence of monitoring and analysis and enhancing the accuracy of the detection of irregular behavior; in combination with the video data acquired by the gantry camera system on the road path, the driving condition of the vehicle can be intuitively observed, necessary evidence support is provided, and the reliability of the judgment is improved; by calculating the irregular risk coefficient, the risk of fee evasion of the target vehicle can be quantified, helping the management department to more effectively identify potential fee evasion behavior and optimize resource allocation; based on the irregular risk coefficient and the road time, a judgment is made and a warning information is issued, so that the relevant departments can take action in time, reduce losses, and improve the efficiency of audit management.
[0030] It should be understood that the content described in the summary section is not intended to limit or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS
[0031] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail the embodiments thereof with reference to the attached drawings. The drawings are intended to be more of a schematic nature, and not limiting the present disclosure. In the drawings, the same or similar reference numerals refer to the same or similar elements, in which:
[0032] Figure 1 A block diagram of a highway audit system based on multi-dimensional big data according to an embodiment of the present disclosure is shown;
[0033] Figure 2 A flowchart of a highway audit method based on multi-dimensional big data according to an embodiment of the present disclosure is shown;
[0034] Figure 3 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0035] To make the objects, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present disclosure.
[0036] In addition, the term "and / or" in this document is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this document generally represents an "or" relationship between the front and rear associated objects.
[0037] Figure 1 A block diagram of a highway auditing system based on multi-dimensional big data according to an embodiment of the present disclosure is shown, which includes:
[0038] The acquisition module 101 is configured to acquire toll data of a target vehicle at any toll station on the highway, the toll data including a toll entry station name, a toll exit station name, and a toll time; determine a toll path of the target vehicle according to the toll entry station name and the toll exit station name; and acquire video data corresponding to each gantry camera collection system on the toll path.
[0039] The calculation module 102 is configured to calculate a violation risk coefficient of the target vehicle according to each of the video data.
[0040] The early warning module 103 is configured to determine whether the target vehicle has an evasion behavior according to the violation risk coefficient and the toll time, and if so, issue a warning information. According to the embodiment of the present disclosure, the toll data of the highway is acquired in real time, so that the toll information of the target vehicle, including the entry and exit station names and times, can be quickly mastered, providing accurate data support for subsequent analysis; according to the toll entry and exit station names, the toll path of the target vehicle can be clearly determined, ensuring the pertinence of monitoring and analysis, and enhancing the accuracy of violation behavior detection; in combination with the video data acquired by the gantry camera system on the toll path, the driving condition of the vehicle can be directly observed, providing necessary evidence support and improving the reliability of judgment; by calculating the violation risk coefficient, the evasion risk of the target vehicle can be quantified, helping the management department to more effectively identify potential evasion behaviors and optimize resource allocation; based on the violation risk coefficient and the toll time, the judgment is made and the warning information is issued, so that the relevant departments can take timely action, reduce losses, and improve the efficiency of auditing management.
[0041] In some embodiments, the toll data further includes a license plate number of the target vehicle, a toll amount, a toll time, and a toll time.
[0042] In some embodiments, the calculating, according to each of the video data, of the risk coefficient of the target vehicle in violation comprises: acquiring, according to each of the video data, a number of times of overspeed of the target vehicle when passing through each of the gantry camera collection systems; and calculating, according to the number of times of overspeed and a corresponding weight, an overspeed risk coefficient of the target vehicle in violation. According to the embodiments of the present application, the driving speed of the target vehicle can be captured in real time through the gantry camera collection system, and the accurate identification of the overspeed behavior can be ensured; the number of times of overspeed of the vehicle at different times and different locations passing through the monitoring point is recorded, and a detailed data set is formed, and the data can be used for subsequent statistical analysis and trend prediction; the overspeed risk coefficient of each vehicle in violation is calculated based on the number of times of overspeed and the set weight, the high-risk vehicle can be identified, the targeted supervision and management can be carried out, and the efficiency of the supervision of the expressway can be improved.
[0043] For example, according to each of the video data, a number M1 of times of overspeed within 20% of the target vehicle when passing through each of the gantry camera collection systems, a number M2 of times of overspeed within 20%-50% of the target vehicle, and a number M3 of times of overspeed above 50% of the target vehicle are acquired.
[0044] The overspeed risk coefficient C of the target vehicle in violation is calculated as follows:
[0045]
[0046] Wherein, Q1 is the first overspeed weight, Q2 is the second overspeed weight, Q3 is the third overspeed weight, M0 is the number of gantry camera collection systems, and K1 is the first conversion coefficient.
[0047] In some embodiments, the calculating, according to each of the video data, of the risk coefficient of the target vehicle in violation further comprises: acquiring, according to each of the video data, a number of times of violation of lane changing of the target vehicle when passing through a target area of each of the gantry camera collection systems; and calculating, according to the number of times of violation of lane changing and a corresponding weight, a risk coefficient of the target vehicle in violation of lane changing. According to the embodiments of the present application, the dynamic behavior of the vehicle can be captured in real time through the gantry camera collection system, and the timely identification of the violation of lane changing can be ensured; the number of times of violation of lane changing of each vehicle in a specific area can be recorded in detail, and a reliable data basis for subsequent analysis can be provided, and the efficiency of the supervision of the expressway can be improved.
[0048] For example, according to each of the video data, a number N1 of times of solid line lane changing without turning on the turn signal, a number N2 of times of solid line lane changing with turning on the turn signal, a number N3 of times of continuous lane changing without turning on the turn signal, a number N4 of times of continuous lane changing with turning on the turn signal, a number N5 of times of dashed line lane changing without turning on the turn signal, and a number N6 of times of occupying the emergency lane of the target vehicle when passing through a target area of each of the gantry camera collection systems are acquired.
[0049] The risk coefficient B of the target vehicle in violation of lane changing is calculated as follows:
[0050]
[0051] wherein, R1 is a first lane-changing weight, R2 is a second lane-changing weight, R3 is a third lane-changing weight, R4 is a fourth lane-changing weight, R5 is a fifth lane-changing weight, R6 is a sixth lane-changing weight, M0 is the number of gantry camera collection systems, and K2 is a second conversion coefficient.
[0052] In some embodiments, the calculation of the risk coefficient of the target vehicle according to the video data further comprises: obtaining, according to the video data, the number of times of illegal driving of the target vehicle in the target area of each gantry camera collection system; and calculating the driving risk coefficient of the target vehicle according to the number of times of illegal driving and the corresponding weight. According to the embodiments of the present application, the gantry camera collection system can capture and record the illegal driving behavior of the vehicle in real time, realize dynamic monitoring of the traffic order, record the number of times of illegal driving of each vehicle in the target area in detail, and provide a reliable basis for subsequent data analysis and evaluation; according to the number of times of illegal driving and the corresponding weight, the driving risk coefficient is calculated to help identify high-risk vehicles and facilitate targeted audit management, thereby improving the efficiency of the highway audit.
[0053] For example, according to the video data, the number of times P1 that the following distance of the target vehicle is less than 60 meters in the target area of each gantry camera collection system, the number of times P2 that the target vehicle uses a mobile phone, and the number of times P3 that the duration of the target vehicle's gaze away from the driving direction is greater than 3 seconds are obtained.
[0054] The driving risk coefficient J of the target vehicle is calculated as follows:
[0055]
[0056] wherein, S1 is a first driving weight, S2 is a second driving weight, S3 is a third driving weight, M0 is the number of gantry camera collection systems, and K3 is a third conversion coefficient.
[0057] In some embodiments, the calculation formula of the risk coefficient of the target vehicle is as follows:
[0058]
[0059] wherein, W is the risk coefficient of the target vehicle, C is the speed violation risk coefficient of the target vehicle, B is the lane-changing violation risk coefficient of the target vehicle, and J is the driving risk coefficient of the target vehicle.
[0060] In some embodiments, the target vehicle is determined to have an evasion behavior according to the risk coefficient of violation and the time of passing through the road, and a warning information is sent out, including: if 0≤W
[0061] For example, if 0≤W<0.2, the target vehicle is determined to have a slight violation during the passing through the road; if 0.2≤W<0.5, the target vehicle is determined to have a general violation during the passing through the road; if 0.5≤W<1, the target vehicle is determined to have a serious violation during the passing through the road.
[0062] In some embodiments, the target vehicle is determined to have an evasion behavior according to the risk coefficient of violation and the time of passing through the road, and a warning information is sent out, further including:
[0063] If the target vehicle has a serious violation during the passing through the road, the theoretical time of passing through the road is calculated according to the road condition;
[0064] According to the time of passing through the road T and the theoretical time of passing through the road T', it is determined whether the target vehicle has an evasion behavior;
[0065] If T>T' *1.5, the target vehicle is determined to have an evasion behavior; if T' *1.5
[0066] Figure 2 A flow chart of the highway auditing method based on multi-dimensional big data according to the embodiments of the present disclosure is shown, which includes:
[0067] S201, obtaining toll data of the target vehicle at any toll station of the expressway, the toll data comprising a toll entry station name, a toll exit station name and a toll time; determining a passing path of the target vehicle according to the toll entry station name and the toll exit station name; and obtaining video data corresponding to each gantry camera collection system on the passing path;
[0068] S202, calculating a violation risk coefficient of the target vehicle according to each of the video data;
[0069] S203, determining whether the target vehicle has an evasion behavior according to the violation risk coefficient and the toll time; and if yes, issuing a warning information.
[0070] In the technical solution of the present disclosure, the acquisition, storage and application of user personal information comply with relevant laws and regulations and do not violate public order and good customs.
[0071] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device and a readable storage medium.
[0072] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.
[0073] The electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes according to a computer program stored in a ROM 302 or a computer program loaded from a storage unit 308 to a RAM 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An I / O interface 305 is also connected to the bus 304.
[0074] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.
[0075] The computing unit 301 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs various methods and processes described above, such as the highway auditing method based on multi-dimensional big data. For example, in some embodiments, the highway auditing method based on multi-dimensional big data can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the highway auditing method based on multi-dimensional big data described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the highway auditing method based on multi-dimensional big data by any other appropriate means, such as by means of firmware.
[0076] Various implementations of the systems and techniques described above herein can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
[0077] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package, or entirely on a remote machine or server.
[0078] In the context of the present disclosure, a readable storage medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. The readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a readable storage medium would include one or more lines of electrical wire, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0079] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
[0080] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
[0081] The computer system can include clients and servers. This relationship can be. The servers are typically remote from the clients with the interactions between them occurring over a communication network. The relationship between client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The servers can be cloud servers, servers of a distributed system, or servers incorporating blockchain.
[0082] It should be understood that the various forms of flow described above can be reordered, steps added or removed. For example, the steps recited in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which are not limited herein.
[0083] The specific implementation described above does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.
Claims
1. A multi-dimensional big data based highway auditing system, characterized in that, The application relates to a highway toll evasion risk early warning method and device. The method comprises the following steps: An acquisition module is used to acquire toll data of a target vehicle at any toll station of a highway, wherein the toll data comprises a toll entry station name, a toll exit station name and a toll time; a passing path of the target vehicle is determined according to the toll entry station name and the toll exit station name; and video data corresponding to each gantry camera collection system on the passing path is acquired; A calculation module is used to calculate a violation risk coefficient of the target vehicle according to the video data; 2. The multi-dimensional big data based highway auditing system as claimed in claim 1, wherein, An early warning module is used to determine whether the target vehicle has an evasion behavior according to the violation risk coefficient and the toll time; if yes, an early warning information is sent out.
3. The multi-dimensional big data based highway auditing system as claimed in claim 2, wherein, The violation risk coefficient of the target vehicle is calculated according to the video data, which comprises the following steps: the number of times of overspeed of the target vehicle when passing through each gantry camera collection system is acquired according to the video data; and an overspeed violation risk coefficient of the target vehicle is calculated according to the number of times of overspeed and corresponding weights.
4. The multi-dimensional big data based highway auditing system as claimed in claim 3, wherein, The violation risk coefficient of the target vehicle is calculated according to the video data, which further comprises the following steps: the number of times of illegal lane changing of the target vehicle when passing through a target area of each gantry camera collection system is acquired according to the video data; and a lane changing violation risk coefficient of the target vehicle is calculated according to the number of times of illegal lane changing and corresponding weights.
5. The multi-dimensional big data based highway auditing system as claimed in claim 4, wherein, The violation risk coefficient of the target vehicle is calculated according to the video data, which further comprises the following steps: the number of times of illegal driving of the target vehicle when passing through a target area of each gantry camera collection system is acquired according to the video data; and a driving violation risk coefficient of the target vehicle is calculated according to the number of times of illegal driving and corresponding weights. The calculation formula of the violation risk coefficient of the target vehicle is as follows:
6. The multi-dimensional big data based highway auditing system as claimed in claim 5, wherein, Wherein, W is the violation risk coefficient of the target vehicle, C is the overspeed violation risk coefficient of the target vehicle, B is the lane changing violation risk coefficient of the target vehicle and J is the driving violation risk coefficient of the target vehicle. The violation risk coefficient and the toll time are used to determine whether the target vehicle has an evasion behavior. If yes, an early warning information is sent out, which comprises the following steps: If 0<=W If W1<=W 7. The multi-dimensional big data based highway auditing system as claimed in claim 6, wherein, If W2<=W If the state of the target vehicle during the passing path is serious violation, the theoretical toll time is calculated according to the passing path; The target vehicle is determined to have an evasion behavior according to the toll time T of the target vehicle and the theoretical toll time T'; If T>T′*1.5, it is determined that the target vehicle has an evasion behavior; if T′*1.5 8. A method for highway auditing based on multi-dimensional big data, characterized in that, It comprises: Obtaining the toll data of the target vehicle at any toll station on the expressway, the toll data comprising a toll entry station name, a toll exit station name and a toll time; determining the toll path of the target vehicle according to the toll entry station name and the toll exit station name; obtaining the video data corresponding to each gantry camera collection system on the toll path; According to each of the video data, calculating the risk coefficient of the target vehicle; According to the risk coefficient and the toll time, determining whether the target vehicle has an evasion behavior; if yes, issuing a warning information.
9. An electronic device, comprising: It comprises: At least one processor; And a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in the expressway auditing system based on multi-dimensional big data according to any one of claims 1-7.
10. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to execute the method in the expressway auditing system based on multi-dimensional big data according to any one of claims 1-7.