Vehicle behavior analysis method, device and equipment and storage medium

Abstract

The invention provides a vehicle behavior analysis method and device, equipment and a storage medium. The method comprises the following steps: determining road network data in a target area, and obtaining positioning data when a vehicle enters the target area and leaves the target area; according to the road network data and the positioning data of the vehicle, judging whether the number of roads passed by the vehicle in a target area exceeds a preset threshold value or not; and if yes, determining that the vehicle has an illegal behavior in the target area. Compared with the prior art, the method has the advantages that a large amount of vehicle behavior data is deeply mined, the behavior pattern features are extracted, the precise recognition capability for hidden induction behaviors is established, and the law enforcement process of law enforcement officers and the experience of tourists are remarkably improved and promoted.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and specifically to a vehicle behavior analysis method, device, equipment and storage medium. Background Art

[0002] Taxi drivers may use various excuses to take tourists to specific shopping locations. For example, they may falsely claim that there is road construction or traffic control ahead and they need to take a detour, passing by some so-called "specialty shops" on the way; or they may say that they have a cooperative relationship with a certain store and can offer discounts to tourists, inducing them to go shopping.

[0003] In market supervision, taxi drivers' behavior of inducing tourists to shop is hidden, such as verbal hints, false recommendations and one-on-one communication, which are difficult to directly identify by conventional means. Therefore, how to identify the behavior of operating vehicle drivers inducing tourists to shop is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0004] The purpose of this application is to provide a vehicle behavior analysis method and device, an electronic device and a computer-readable storage medium.

[0005] The first aspect of the present application provides a vehicle behavior analysis method, comprising:

[0006] Determine the road network data within the target area;

[0007] Obtaining the positioning data of the vehicle from the time it enters the target area to the time it leaves the target area;

[0008] It is determined whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold value based on the road network data and the positioning data of the vehicle; if so, it is determined that the vehicle has violated the regulations in the target area.

[0009] In a possible implementation, determining the road network data in the target area includes:

[0010] Obtaining boundary data of a target area and road network data within the boundary data;

[0011] The boundary data and the road network data are converted from line elements into a series of point elements, and the positioning data corresponding to all the point elements are obtained.

[0012] In a possible implementation, the method further includes:

[0013] Check whether the boundary data self-intersects, and split the boundary data that self-intersects.

[0014] In a possible implementation, the acquiring of the positioning data of the vehicle from the time when the vehicle enters the target area to the time when the vehicle leaves the target area includes:

[0015] Determining whether the vehicle has entered the target area based on the boundary data and the positioning data of the vehicle;

[0016] When the vehicle first enters the boundary of the target area, the entry time is recorded;

[0017] When the vehicle exits the boundary of the target area, the exit time is recorded;

[0018] Acquire positioning data of the vehicle between the entry time and the exit time.

[0019] In a possible implementation, judging whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold according to the road network data and the positioning data of the vehicle includes:

[0020] Splitting the road network data into a plurality of logically independent line segments, each of the line segments corresponding to a road;

[0021] Based on the positioning data of the vehicle and the positioning data corresponding to the road network data, the target road closest to the vehicle is calculated; if the closest distance between the vehicle and the target road is less than a preset distance and the vehicle stays in the target road for longer than a preset time, it is considered that the vehicle has traveled on the target road.

[0022] A second aspect of the present application provides a vehicle behavior analysis device, comprising:

[0023] A determination module, used to determine the road network data within the target area;

[0024] An acquisition module is used to acquire the positioning data of the vehicle from the time it enters the target area to the time it leaves the target area;

[0025] The judgment module is used to judge whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold according to the road network data and the positioning data of the vehicle; if so, it is determined that the vehicle has violated the regulations in the target area.

[0026] In a possible implementation, the determining module is specifically configured to:

[0027] Obtaining boundary data of a target area and road network data within the boundary data;

[0028] The boundary data and the road network data are converted from line elements into a series of point elements, and the positioning data corresponding to all the point elements are obtained.

[0029] In a possible implementation manner, the determining module is further specifically configured to:

[0030] Check whether the boundary data self-intersects, and split the boundary data that self-intersects.

[0031] In a possible implementation, the acquisition module is specifically used to:

[0032] Determining whether the vehicle has entered the target area based on the boundary data and the positioning data of the vehicle;

[0033] When the vehicle first enters the boundary of the target area, the entry time is recorded;

[0034] When the vehicle exits the boundary of the target area, the exit time is recorded;

[0035] Acquire positioning data of the vehicle between the entry time and the exit time.

[0036] In a possible implementation, the determination module is specifically configured to:

[0037] Splitting the road network data into a plurality of logically independent line segments, each of the line segments corresponding to a road;

[0038] Based on the positioning data of the vehicle and the positioning data corresponding to the road network data, the target road closest to the vehicle is calculated; if the closest distance between the vehicle and the target road is less than a preset distance and the vehicle stays in the target road for longer than a preset time, it is considered that the vehicle has traveled on the target road.

[0039] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle behavior analysis method described in the first aspect of the present application.

[0040] The fourth aspect of the present application provides a computer-readable storage medium having computer-readable instructions stored thereon, and the computer-readable instructions can be executed by a processor to implement the vehicle behavior analysis method described in the first aspect of the present application.

[0041] The vehicle behavior analysis method, device, equipment and storage medium provided by the present application obtain the positioning data of the vehicle from entering the target area to leaving the target area by determining the road network data in the target area; determine whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold based on the road network data and the positioning data of the vehicle; if so, determine that the vehicle has violated the regulations in the target area. Compared with the prior art, the present application deeply mines a large amount of vehicle driving behavior data, extracts the driving behavior pattern characteristics of the vehicle in the target area, and establishes the ability to accurately identify hidden induced behaviors in the target area by monitoring the driving trajectory of the vehicle in the target area such as a scenic spot. The application of the present application has brought significant improvements and enhancements to the law enforcement process of law enforcement personnel and the experience of tourists. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0043] Figure 1 A schematic diagram of a vehicle behavior analysis method provided by the present application is shown;

[0044] Figure 2 The preprocessing process of the vehicle positioning data, the boundary data of the target area and the road network data provided by the present application is shown;

[0045] Figure 3 The specific process of judging whether a vehicle has abnormal behavior of inducing shopping in a scenic area based on the present application is shown;

[0046] Figure 4 A schematic diagram of the structure of a vehicle behavior analysis device provided by the present application is shown;

[0047] Figure 5 A schematic structural diagram of an electronic device provided by the present application is shown. DETAILED DESCRIPTION

[0048] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by technicians in the field to which this application belongs.

[0050] In addition, the terms "first" and "second" etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0051] In order to facilitate the understanding of this application, the prior art is first analyzed as follows:

[0052] 1. Taxi operation supervision technology. In terms of taxi operation management, some supervision technologies already exist. For example, taxi supervision technology based on the GPS positioning system can obtain the location information of taxis in real time, so that the regulatory authorities can monitor the driving trajectory and mileage compliance of taxis to ensure that taxis follow the prescribed routes, do not refuse to pick up passengers and other basic operating regulations. However, this technology mainly focuses on the routine operation management of taxis, and lacks targeted monitoring and analysis capabilities for the specific behavior of taxi drivers illegally inducing tourists to shop.

[0053] 2. Tourism market supervision technology. There are also relevant technical means in tourism market supervision. At present, tourism market supervision mainly focuses on the business behavior of travel agencies and the service quality of scenic spots. For the highly concealed behavior of taxis illegally inducing tourists to shop in scenic spots, the existing tourism market supervision technology is difficult to effectively identify and supervise. Because these technologies are more about supervision from the macro level of the overall tourism market, lack of detailed analysis of the bad behavior of specific transportation tools in tourism scenarios.

[0054] 3. Traffic market supervision technology. Data mining technology has certain applications in the field of traffic supervision. Data mining can be used to analyze vehicle hot spot behaviors, operating behaviors, etc. However, at present, these applications are mainly focused on the analysis of normal supervision behaviors, and there are basically no applications that focus on solving problems in a certain industry. The mining of the hidden patterns behind the illegal behavior of taxis inducing tourists to shop has not been involved, and there is a lack of behavioral pattern mining technology specifically targeting such illegal behaviors.

[0055] The prior art has the following problems and disadvantages:

[0056] 1. The monitoring of illegal inducement behavior is not targeted enough. Although the existing taxi operation supervision technology can supervise the basic operation of taxis, there is no special monitoring mechanism for the specific behavior of illegally inducing tourists to shop. Taxi drivers may use verbal hints, deliberately take detours through certain shopping spots, etc. to induce tourists to shop, but the existing conventional supervision methods such as GPS positioning cannot accurately identify whether these behaviors are illegal inducement, making it difficult to effectively curb such bad behavior.

[0057] 2. There are blind spots in tourism market supervision. Tourism market supervision has blind spots when it comes to taxis illegally inducing tourists to shop. Because this behavior is somewhat hidden, it is not as easy to detect as travel agencies’ illegal operations or scenic spot service quality issues. Existing tourism market supervision technology mainly focuses on other links in the tourism industry chain, and lacks attention to taxis, which are highly mobile individuals, inducing tourists to shop in scenic spots, making it difficult to detect and deal with such illegal behavior in a timely manner, which damages the rights and interests of tourists.

[0058] 3. Lack of specialized technology for behavioral pattern mining. The application of existing data mining technology in the tourism field has not focused on mining the behavior patterns of taxis that violate regulations and induce tourists to shop. Such violations often involve complex factors such as the interaction between drivers and tourists, the relationship between taxi routes and shopping spots, and the existing data mining technology cannot effectively extract features that can reflect such violation patterns from massive tourism-related data, making it difficult to analyze and warn such violations from the perspective of behavioral patterns.

[0059] In view of this, embodiments of the present application provide a vehicle behavior analysis method and device, an electronic device, and a computer-readable storage medium, which are described below in conjunction with the accompanying drawings.

[0060] Figure 1 The flowchart of a vehicle behavior analysis method provided by an embodiment of the present application is shown. The execution subject of the present embodiment may be a vehicle management system, which may be implemented based on software and / or hardware, and the present application does not limit this. Figure 1 As shown, the method specifically comprises the following steps:

[0061] S101, determining the road network data in the target area;

[0062] Specifically, the target area may be a tourist attraction or a shopping attraction, which is not limited in this application. The road network data is the detailed data of the roads in the target area.

[0063] The above step S101 can be specifically implemented as follows: obtaining boundary data of the target area and road network data within the boundary data; converting the boundary data and the road network data from line elements into a series of point elements, and obtaining positioning data corresponding to all point elements.

[0064] In the embodiment of the present application, the boundary data of the target area is first pre-processed by screening and correction. The boundary data is provided by the user and may be non-standard, self-intersecting, inconsistent delineation order of different scenic spots, etc. It is necessary to split the self-intersecting boundaries and filter the non-standard boundary data, such as data with only two points. The boundary data is unified in latitude and longitude format and formed into polygons.

[0065] Therefore, in some embodiments, the above method may further include the steps of: checking whether the boundary data self-intersects, and splitting the boundary data that self-intersects. For example, for scenic area boundary data, it is necessary to uniformly split the scenic area boundary data into independent positioning points and check whether the boundary data self-intersects. If self-intersects, split them into unconnected polygon data respectively, integrate them into standard polygons, and then convert them into longitude and latitude points. Split the urban roads in the road network data into irregular lines composed of multiple points, and then convert them into longitude and latitude points.

[0066] S102, obtaining positioning data of the vehicle from the time it enters the target area to the time it leaves the target area;

[0067] Specifically, the above step S102 can be implemented as follows: determine whether the vehicle enters the target area based on the boundary data and the vehicle positioning data; when the vehicle enters the boundary of the target area for the first time, record the entry time; when the vehicle exits the boundary of the target area, record the exit time; and obtain the positioning data of the vehicle between the entry time and the exit time.

[0068] In the embodiment of the present application, the vehicle positioning data is data generated by the vehicle positioning device, such as GPS data. After obtaining the vehicle positioning data, the positioning data is first pre-processed. For example, the latitude and longitude format in the GPS data is not a floating point number, which needs to be processed into a standard format of latitude and longitude; the positioning time is a timestamp, which needs to be standardized in Beijing time, and the data that contains the license plate number, latitude and longitude, and positioning time in the positioning data is filtered out.

[0069] For example, for vehicle GPS positioning data, the positioning longitude and latitude data need to be uniformly processed into floating-point types, such as processing (114279508, 30601878) as (114.279508, 30.601878), and the positioning time is converted into Beijing time, accurate to seconds, and the format is unified, such as processing 1727798399000 as "2024-10-01 23:59:59", ensuring that the longitude and latitude data format after the boundary and road network data are converted is consistent with the vehicle positioning data format.

[0070] Vehicle positioning data screening: uniformly screen out vehicle information whose longitude and latitude cannot be empty and are not 0, and whose license plate number conforms to the regular expression; after the screening is completed, a positioning data fast calculation method based on spatial data dimensionality reduction and multivariate algorithm integration will be used to quickly determine whether the vehicle positioning data exists in the target area. If the positioning data exists in the target area, it means that this vehicle positioning data is valid.

[0071] For ease of understanding, this application Figure 2 The preprocessing process of vehicle positioning data, boundary data of target area and road network data is shown.

[0072] S103, judging whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold according to the road network data and the positioning data of the vehicle; if so, determining that the vehicle has violated the regulations in the target area. The preset threshold can be set to 4, 5, 6, etc.

[0073] This step is to mine vehicle behavior patterns based on the road network data of the target area and the vehicle positioning data to determine whether the vehicle has violated any regulations in the target area.

[0074] In the above steps, the purpose of calculating the number of roads that the vehicle has traveled in the target area is to determine whether the vehicle has suspicious behavior of repeatedly traveling on the roads in the area within a short period of time. Therefore, there are many ways to calculate the number of roads that the vehicle has traveled in the target area, one is the number of different roads traveled, the other is the number of repetitions of the same road, and of course other ways are also possible, which are not limited in this application.

[0075] For example, one judgment method is that when a vehicle repeatedly drives on multiple roads in a scenic area within a short period of time, it is considered that the vehicle is inducing passengers to shop in the scenic area. The judgment time can be set to 5-

[0076] For example, if a vehicle repeatedly drives on multiple roads in a scenic area within 5 minutes, it is very likely to take passengers to different shopping locations to induce shopping. Therefore, suspicious vehicles can be identified based on the vehicle's driving behavior pattern characteristics.

[0077] Another way to judge is that when a vehicle repeatedly drives on a road in a scenic area for many times in a short period of time, it can be considered that the vehicle is inducing passengers to shop in the scenic area. For example, if a vehicle repeatedly drives around a closed polygon path in a scenic area for more than 4 times within 5 minutes, it is very likely that it will take passengers to a specific shopping location to induce shopping. Therefore, suspicious vehicles can be identified based on the vehicle's driving behavior pattern characteristics.

[0078] In some embodiments, the driving trajectory within the scenic area can be fitted in real time based on the vehicle's GPS trajectory. When it is identified that the vehicle's driving path within the target area forms a closed polygon, counting begins. When the number of repeated driving in the closed polygon is greater than 4 times within a preset time, suspicious vehicles can be identified based on the vehicle's driving behavior pattern characteristics.

[0079] Specifically, the above step S103 can be implemented as follows:

[0080] Splitting the road network data into a plurality of logically independent line segments, each of the line segments corresponding to a road;

[0081] According to the positioning data of the vehicle and the positioning data corresponding to the road network data, the target road closest to the vehicle is calculated. If the closest distance between the vehicle and the target road is less than a preset distance and the vehicle stays in the target road for longer than a preset time, it is considered that the vehicle has traveled on the target road. Due to certain errors in positioning data, the preset distance can be set to 10 meters. The preset time can be set to 120 seconds.

[0082] The number of roads traveled by the vehicle in the target area is counted, and it is determined whether the number of roads traveled by the vehicle exceeds a preset threshold.

[0083] For example, vehicle positioning data is matched to roads: each road is represented by a series of ordered longitude and latitude points that define the geometry of the road, and each longitude and latitude point generates a digital signature. The B-tree data structure can be used to organize the digital signatures corresponding to these roads. The B-tree is a self-balancing tree data structure that keeps data in order and allows fast insertion, deletion, and search operations. In this case, each node of the B-tree can store multiple digital signatures, and the road containing the specific longitude and latitude points can be quickly located in the tree. When determining the road to which the vehicle positioning data belongs, trigonometric functions are used to calculate the nearest intersection of the vehicle positioning data and the road, and the Euclidean distance formula is used to calculate the intersection of the vehicle positioning data and the road. The closest distance is calculated based on the positioning data. Due to the certain error in positioning data, if the closest distance is less than 10 meters and the vehicle stays on the road for more than 120 seconds, it is considered that the vehicle positioning data belongs to this road, that is, the vehicle has traveled on this road. Similarly, the number of roads that the vehicle has traveled in the target area is calculated. For example, if the vehicle repeatedly travels on more than 4 roads in the scenic area, it means that the vehicle has induced shopping in the scenic area. For ease of understanding, this application Figure 3 The specific process of judging whether a vehicle has abnormal behavior of inducing shopping in a scenic area based on the present application is shown.

[0084] The beneficial effects of the vehicle behavior analysis method provided by the embodiment of the present application are as follows:

[0085] The method of the present invention, through the vehicle positioning data, urban road data and scenic area boundary longitude and latitude data, combined with the algorithm to determine whether the vehicle has induced shopping behavior in the scenic area, the application of this technology has brought significant improvement and enhancement to the law enforcement process of law enforcement personnel and the experience of tourists. Traditional law enforcement means often rely on manual inspections and complaints and reports, which are not only inefficient, but also have serious lags, and are easily interfered by human factors, resulting in unfair, inaccurate and untimely law enforcement. Now, through the automatic monitoring of the algorithm, law enforcement personnel can quickly locate taxis suspected of inducing shopping, so as to take measures in time. Such technical means not only improve the response speed of law enforcement, but also enhance the fairness of law enforcement. Thereby reducing the overall cost of law enforcement. Through effective supervision and crackdown on illegal acts, social contradictions and disputes caused by illegal acts such as induced shopping can be reduced. By reducing illegal acts such as induced shopping, tourists can enjoy the travel process more at ease without worrying about being defrauded or forced to consume. The probability of tourists being deceived is reduced, and the satisfaction and travel experience of tourists are improved, thereby helping to enhance the image and attractiveness of the entire tourism market, which is of great significance for promoting the healthy development of the tourism industry.

[0086] In the above embodiment, a vehicle behavior analysis method is provided. Correspondingly, the present application also provides a vehicle behavior analysis device. The vehicle behavior analysis device provided in the embodiment of the present application can implement the above vehicle behavior analysis method. The vehicle behavior analysis device can be implemented by software, hardware, or a combination of software and hardware. For example, the vehicle behavior analysis device can include integrated or separate functional modules or units to perform the corresponding steps in the above methods. Please refer to Figure 4 , which shows a schematic diagram of a vehicle behavior analysis device provided by an embodiment of the present application. Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described below is only illustrative.

[0087] like Figure 4 As shown, the vehicle behavior analysis device 10 may include:

[0088] A determination module 101 is used to determine the road network data in the target area;

[0089] The acquisition module 102 is used to acquire the positioning data of the vehicle from the time when the vehicle enters the target area to the time when the vehicle leaves the target area;

[0090] The judgment module 103 is used to judge whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold based on the road network data and the positioning data of the vehicle; if so, it is determined that the vehicle has violated the regulations in the target area.

[0091] In a possible implementation, the determining module 101 is specifically configured to:

[0092] Obtaining boundary data of a target area and road network data within the boundary data;

[0093] The boundary data and the road network data are converted from line elements into a series of point elements, and the positioning data corresponding to all the point elements are obtained.

[0094] In a possible implementation, the determining module 101 is further specifically configured to:

[0095] Check whether the boundary data self-intersects, and split the boundary data that self-intersects.

[0096] In a possible implementation, the acquisition module 102 is specifically configured to:

[0097] Determining whether the vehicle has entered the target area based on the boundary data and the positioning data of the vehicle;

[0098] When the vehicle first enters the boundary of the target area, the entry time is recorded;

[0099] When the vehicle exits the boundary of the target area, the exit time is recorded;

[0100] Acquire positioning data of the vehicle between the entry time and the exit time.

[0101] In a possible implementation, the determining module 103 is specifically configured to:

[0102] Splitting the road network data into a plurality of logically independent line segments, each of the line segments corresponding to a road;

[0103] Based on the positioning data of the vehicle and the positioning data corresponding to the road network data, the target road closest to the vehicle is calculated; if the closest distance between the vehicle and the target road is less than a preset distance and the vehicle stays in the target road for longer than a preset time, it is considered that the vehicle has traveled on the target road.

[0104] The vehicle behavior analysis device provided in the embodiment of the present application determines the road network data in the target area, obtains the positioning data of the vehicle from entering the target area to leaving the target area; determines whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold based on the road network data and the positioning data of the vehicle; if so, determines that the vehicle has violated the regulations in the target area. Compared with the prior art, the present application deeply mines a large amount of vehicle behavior data, extracts behavior pattern features, and establishes the ability to accurately identify hidden induced behaviors. The application of the present application has brought significant improvements and enhancements to the law enforcement process of law enforcement personnel and the tourist experience.

[0105] An embodiment of the present application also provides an electronic device corresponding to the vehicle behavior analysis method provided in the aforementioned embodiment. The electronic device may be a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the aforementioned vehicle behavior analysis method.

[0106] Please refer to Figure 5 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 5 As shown, the electronic device 20 includes: a processor 200, a memory 201, a bus 202 and a communication interface 203, and the processor 200, the communication interface 203 and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can be run on the processor 200, and when the processor 200 runs the computer program, the vehicle behavior analysis method provided in any of the aforementioned embodiments of the present application is executed.

[0107] The memory 201 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0108] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs, and the processor 200 executes the program after receiving an execution instruction. The vehicle behavior analysis method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 200, or implemented by the processor 200.

[0109] The processor 200 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 200. The above processor 200 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0110] The electronic device provided in the embodiment of the present application and the vehicle behavior analysis method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0111] An embodiment of the present application also provides a computer-readable storage medium corresponding to the vehicle behavior analysis method provided in the aforementioned embodiment. The computer-readable storage medium may be a CD on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the vehicle behavior analysis method provided in any of the aforementioned embodiments.

[0112] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0113] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the vehicle behavior analysis method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application.

Claims

1. A vehicle behavior analysis method, characterized in that: include: Determine the road network data within the target area; Obtaining the positioning data of the vehicle from the time it enters the target area to the time it leaves the target area; Determining whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold according to the road network data and the positioning data of the vehicle; If so, it is determined that the vehicle has violated the regulations in the target area.

2. The method according to claim 1, characterized in that The determining of the road network data in the target area includes: Obtaining boundary data of a target area and road network data within the boundary data; The boundary data and the road network data are converted from line elements into a series of point elements, and the positioning data corresponding to all the point elements are obtained.

3. The method according to claim 2, characterized in that The method further comprises: Check whether the boundary data self-intersects, and split the boundary data that self-intersects.

4. The method according to claim 2, characterized in that: The obtaining of the positioning data of the vehicle from entering the target area to leaving the target area includes: Determining whether the vehicle has entered the target area based on the boundary data and the positioning data of the vehicle; When the vehicle first enters the boundary of the target area, the entry time is recorded; When the vehicle exits the boundary of the target area, the exit time is recorded; Acquire positioning data of the vehicle between the entry time and the exit time.

5. The method according to claim 2, characterized in that: The determining, based on the road network data and the positioning data of the vehicle, whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold value includes: Splitting the road network data into a plurality of logically independent line segments, each of the line segments corresponding to a road; Based on the positioning data of the vehicle and the positioning data corresponding to the road network data, the target road closest to the vehicle is calculated; if the closest distance between the vehicle and the target road is less than a preset distance and the vehicle stays in the target road for longer than a preset time, it is considered that the vehicle has traveled on the target road.

6. A vehicle behavior analysis device, characterized in that: include: A determination module, used to determine the road network data within the target area; An acquisition module is used to acquire the positioning data of the vehicle from the time it enters the target area to the time it leaves the target area; A judgment module, used to judge whether the number of roads traveled by the vehicle in the target area exceeds a preset threshold according to the road network data and the positioning data of the vehicle; If so, it is determined that the vehicle has violated the regulations in the target area.

7. The device according to claim 6, characterized in that The determination module is specifically used for: Obtaining boundary data of a target area and road network data within the boundary data; The boundary data and the road network data are converted from line elements into a series of point elements, and the positioning data corresponding to all the point elements are obtained.

8. The device according to claim 7, characterized in that The judgment module is specifically used for: Splitting the road network data into a plurality of logically independent line segments, each of the line segments corresponding to a road; Based on the positioning data of the vehicle and the positioning data corresponding to the road network data, the target road closest to the vehicle is calculated; if the closest distance between the vehicle and the target road is less than a preset distance and the vehicle stays in the target road for longer than a preset time, it is considered that the vehicle has traveled on the target road.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1 to 5.

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