Internet of vehicles situation detection method and device, equipment, storage medium and product

Abstract

The invention discloses an Internet of Vehicles situation detection method, apparatus and device, a storage medium and a product. The method comprises the steps of obtaining a GIS digital map of a target area and DPI network vehicle machine data of all vehicles in the target area; according to the DPI network in-vehicle information machine data, obtaining in-vehicle information machine basic information of the target area; according to the vehicle-mounted terminal basic information, generating a vehicle-mounted terminal congestion queue diagram of the target area; and generating a vehicle congestion thermodynamic diagram of the target area according to the vehicle congestion queue diagram and the GIS digital map. Based on DPI real-time network vehicle machine data and longitude and latitude information of a vehicle, a vehicle enterprise client does not need to provide other data source support, the area where the vehicle is located is calculated according to the physical station position reported by the vehicle machine and the community signal coverage range, the flow or number of vehicles in each area is obtained, and a vehicle machine congestion thermodynamic diagram is drawn.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking technology, and in particular to a vehicle networking situation detection method, device, equipment, storage medium and product. Background Art

[0002] Detecting the situation of the Internet of Vehicles can intuitively display the distribution and density of vehicles in a specific area or road, as well as the traffic flow in different time periods. The existing Internet of Vehicles situation detection construction method generally collects the location information of vehicles through Internet of Vehicles equipment, including the latitude and longitude, timestamp, speed, direction and other information of the vehicle, pre-processes the data, aggregates them by time period (such as hourly, daily) or geographical area (such as grid, area, road segment), and then maps the data to the map. According to the density or number of vehicles, different colors or sizes are set to represent different heats, and multiple heat maps can be generated according to the time dimension or space dimension to show the dynamic changes of traffic flow. Therefore, the existing Internet of Vehicles situation detection method requires data analysis and processing from multiple data sources such as vehicle data and road segmentation data to generate a vehicle congestion heat map, thereby showing the vehicle distribution, density and flow of the area. Due to the scattered data sources, it is necessary for car companies to cooperate in providing data, the approval process is complicated, and a lot of time is spent on data preparation, which will bring difficulties to the situation detection of the Internet of Vehicles. Summary of the invention

[0003] The present invention provides a method, device, equipment, storage medium and product for detecting the situation of Internet of Vehicles. Based on DPI real-time network vehicle data and the latitude and longitude information of the vehicle, the number and speed of vehicles in each area are detected, thereby drawing a vehicle congestion heat map.

[0004] In order to achieve the above object, an embodiment of the present invention provides a method for detecting a situation in an Internet of Vehicles, comprising:

[0005] Obtain the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; wherein the DPI network vehicle data includes real-time vehicle data and historical vehicle data within a certain period of time; the real-time / historical vehicle data includes the collection time, IoT card ID, cell identification and tracking area code of all vehicles;

[0006] According to the DPI network vehicle data, the vehicle basic information of the target area is obtained; wherein the vehicle basic information includes real-time vehicle flow, real-time speed, historical vehicle flow and historical speed;

[0007] Generate a vehicle-machine congestion queue diagram of the target area according to the vehicle-machine basic information;

[0008] A vehicle-machine congestion heat map of the target area is generated according to the vehicle-machine congestion queue map and the GIS digital map.

[0009] As an improvement of the above solution, generating a vehicle-machine congestion heat map of the target area according to the vehicle-machine congestion queue map and the GIS digital map includes:

[0010] Calculating the road area and the ratio of the road area to the total area of ​​the target area according to the GIS digital map;

[0011] If the ratio is greater than a preset ratio threshold, vehicles near the road network of the target area are converged into the road network according to the vehicle position information and speed of the vehicle congestion queue map, and a vehicle congestion heat map of the target area is generated.

[0012] As an improvement of the above solution, obtaining the vehicle computer basic information of the target area according to the DPI network vehicle computer data includes:

[0013] According to the real-time vehicle computer data, the vehicle computer status corresponding to all vehicles at the current moment is obtained; wherein the vehicle computer status includes normal driving, stopping, disconnection or no network;

[0014] According to the vehicle computer state and the real-time vehicle computer data, target real-time vehicle computer data corresponding to the normal driving is obtained;

[0015] The basic information of the vehicle computers in the target area is obtained according to the historical vehicle computer data and the target real-time vehicle computer data.

[0016] As an improvement of the above solution, obtaining the target real-time vehicle computer data corresponding to the normal driving according to the vehicle computer state and the real-time vehicle computer data includes:

[0017] According to the real-time vehicle computer data and the preset driving and stopping conditions, obtaining the real-time vehicle computer data corresponding to the driving and stopping;

[0018] According to the real-time vehicle computer data and the preset disconnection or no network condition, the real-time vehicle computer data corresponding to the disconnection or no network is obtained;

[0019] The real-time vehicle machine data corresponding to the stopping and disconnecting or no network is eliminated to obtain the target real-time vehicle machine data corresponding to the normal driving.

[0020] As an improvement of the above solution, the basic vehicle information of the target area is obtained according to the historical vehicle data and the target real-time vehicle data, including:

[0021] According to the historical vehicle data, the historical vehicle flow and speed of the target area are obtained;

[0022] According to the target real-time vehicle machine data, the number of target vehicle machines in the target area is counted to obtain the real-time vehicle flow in the target area;

[0023] The real-time speed of the target area is calculated according to the collection time, tracking area code and cell identifier in the target real-time vehicle data.

[0024] As an improvement of the above solution, after generating the vehicle congestion heat map of the target area, the method further includes:

[0025] According to the vehicle congestion heat map, the traffic flow information of the target area is obtained to control the vehicle driving.

[0026] In order to achieve the above object, an embodiment of the present invention provides a vehicle networking situation detection device, comprising:

[0027] The relevant data acquisition module is used to obtain the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; wherein the DPI network vehicle data includes real-time vehicle data and historical vehicle data within a certain period of time; the real-time / historical vehicle data includes the collection time, IoT card ID, cell identification and tracking area code of all vehicles;

[0028] A vehicle information acquisition module, used to obtain the vehicle basic information of the target area according to the DPI network vehicle data; wherein the vehicle basic information includes real-time vehicle flow, real-time speed, historical vehicle flow and historical speed;

[0029] A congestion queue map generating module, used for generating a vehicle machine congestion queue map of the target area according to the vehicle machine basic information;

[0030] The congestion heat map generation module is used to generate a vehicle congestion heat map of the target area according to the vehicle congestion queue map and the GIS digital map.

[0031] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a corresponding vehicle network situation detection device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned vehicle network situation detection method when executing the computer program.

[0032] In order to achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned Internet of Vehicles situation detection method.

[0033] To achieve the above objectives, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the above-mentioned vehicle network situation detection method.

[0034] Compared with the prior art, the embodiment of the present invention discloses a method, device, equipment, storage medium and product for detecting the situation of Internet of Vehicles, which obtains the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; obtains the basic vehicle information of the target area according to the DPI network vehicle data; generates a vehicle congestion queue map of the target area according to the basic vehicle information; generates a vehicle congestion heat map of the target area according to the vehicle congestion queue map and the GIS digital map. Based on the DPI real-time network vehicle data and the latitude and longitude information of the vehicle, there is no need for the car enterprise customer to provide other data source support. The area where the vehicle is located is calculated according to the physical site location reported by the vehicle and the cell signal coverage range, and the flow or number of vehicles in each area is obtained to draw a vehicle congestion heat map; at the same time, according to the reporting frequency of the vehicle and the mobile switching between cells, comparative heat maps of different time nodes can be obtained to show the dynamic changes of traffic flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flowchart of a method for detecting a situation in an Internet of Vehicles provided by an embodiment of the present invention;

[0036] Figure 2 A vehicle congestion heat map provided by an embodiment of the present invention;

[0037] Figure 3 A vehicle-machine congestion queue diagram provided by an embodiment of the present invention;

[0038] Figure 4 It is a structural schematic diagram of a vehicle networking situation detection device provided by an embodiment of the present invention;

[0039] Figure 5 It is a structural block diagram of a vehicle networking situation detection device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] It should be noted that the terms "comprises" and "specifically" and any variations of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0042] See also Figure 1 , Figure 1 : is a flow chart of a method for detecting a situation of an Internet of Vehicles provided by an embodiment of the present invention, the method for detecting a situation of an Internet of Vehicles comprising:

[0043] S1, obtaining the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; wherein the DPI network vehicle data includes real-time vehicle data and historical vehicle data within a certain period of time; the real-time / historical vehicle data includes the collection time, IoT card ID, cell identification and tracking area code of all vehicles;

[0044] S2, obtaining basic vehicle information of the target area according to the DPI network vehicle data; wherein the basic vehicle information includes real-time vehicle flow, real-time speed, historical vehicle flow and historical speed;

[0045] S3, generating a vehicle congestion queue map of the target area according to the vehicle basic information;

[0046] S4, generating a vehicle-machine congestion heat map of the target area according to the vehicle-machine congestion queue map and the GIS digital map.

[0047] Exemplarily, the vehicle networking situation detection method described in the embodiment of the present invention can be implemented as a code through a Web service interface for external application calls. The calling interface needs to transmit the relevant data of the vehicle machine and respond to the specific road congestion information in real time. For example, the input parameter information returns the relevant message field information. The input parameter information and message field information are shown in Table 1 and Table 2.

[0048] Table 1 Input parameter information

[0049] Fields Field Chinese name type Is it necessary to transmit StatisticalTime Collection time String yes ICCID IoT Card ID String yes CellID Access cell ID String yes TAC Tracking (location) area code Integer yes

[0050] Table 2 Message field information

[0051] Fields Field Chinese name type Remark status Request Status Integer 200 for success, otherwise failure message Returned information String As described in the error timestamp Data generation timestamp String road_segments Road segment list List segment_id Unique segment identifier String start_point The latitude and longitude coordinates of the starting point of the segment Dict end_point The latitude and longitude coordinates of the end point of the segment Dict congestion_level Congestion Level Integer Level 1-10

[0052] Specifically, the step S4 includes:

[0053] S41, calculating the road area of ​​the target area and the ratio of the road area to the total area according to the GIS digital map;

[0054] S42, if the ratio is greater than a preset ratio threshold, vehicles near the road network of the target area are converged into the road network according to the vehicle position information and speed of the vehicle congestion queue map, and a vehicle congestion heat map of the target area is generated.

[0055] Exemplarily, taking the preset ratio threshold of 60% as an example (the preset ratio threshold can be set as needed), the generated vehicle congestion queue map is integrated with the GIS digital map, and the ratio of the road area of ​​the community in the target area to the total area of ​​the community is calculated. If the road area accounts for more than 60% of the total area, the speed data of all vehicles in the community are counted, and the average speed μ and standard deviation σ are calculated. According to the normal distribution principle, the exclusion range of speed anomalies is set to (μ-3σ, μ+3σ), that is, any vehicle whose speed exceeds this range is regarded as an anomaly. According to the vehicle position information and normal vehicle speed of the vehicle congestion queue map, the corresponding vehicles (such as vehicles outside the road network and whose vehicle speed is within the normal range) are converged to the nearest road network to obtain the vehicle congestion heat map of the target area, such as Figure 2 As shown, Figure 2 This is a vehicle congestion heat map provided by an embodiment of the present invention. The embodiment of the present invention can ensure the accuracy of data and avoid some vehicles being displayed as driving in a community or around a community instead of driving on the main road.

[0056] Specifically, the step S2 includes:

[0057] S21, obtaining the vehicle computer status corresponding to all vehicles at the current moment according to the real-time vehicle computer data; wherein the vehicle computer status includes normal driving, stopping, disconnection or no network;

[0058] S22, obtaining target real-time vehicle computer data corresponding to the normal driving according to the vehicle computer state and the real-time vehicle computer data;

[0059] S23, obtaining basic vehicle computer information of the target area according to the historical vehicle computer data and the target real-time vehicle computer data.

[0060] Specifically, the step S22 includes:

[0061] According to the real-time vehicle computer data and the preset driving and stopping conditions, obtaining the real-time vehicle computer data corresponding to the driving and stopping;

[0062] According to the real-time vehicle computer data and the preset disconnection or no network condition, the real-time vehicle computer data corresponding to the disconnection or no network is obtained;

[0063] The real-time vehicle machine data corresponding to the stopping and the disconnection or the no network are eliminated to obtain the target real-time vehicle machine data corresponding to the normal driving.

[0064] Exemplarily, first, the real-time vehicle data set P acquired within the certain time T is preprocessed, cleaned, and abnormal data is processed. Abnormal data includes the following situations: shutdown (user actively goes offline, there is reported data, and the vehicle status is detch), disconnection (network abnormality causes passive offline, no reported data), no network (the vehicle enters the network signal blind spot, no reported data). When the vehicle is shut down, there is reported data, and the data can be filtered by the vehicle status. There is no reported data when disconnected and without network, and it is necessary to use an algorithm to determine whether it is abnormal. For example, a timer is set, and the unit timing time is T. The preset stop condition: suppose the center point of the cell reported by the vehicle at time t1 is P1, and the center point of the cell reported before T time is P0, and the distance between the two positions is D(P1, P0), then the preset stop condition can be expressed as: [D(P1, P0)<100]; disconnection or no network condition: suppose the last reporting time of the vehicle is T l , the current time is T n , then the disconnection or no network condition can be expressed as: [T n -T l >T]. After the above algorithm logic processing, the current driving state of the vehicle can be obtained, so as to judge whether the vehicle is stopped, eliminate invalid data, and obtain the target real-time vehicle data in normal driving state.

[0065] Specifically, the step S23 includes:

[0066] According to the historical vehicle data, the historical vehicle flow and speed of the target area are obtained;

[0067] According to the target real-time vehicle machine data, the number of target vehicle machines in the target area is counted to obtain the real-time vehicle flow in the target area;

[0068] The real-time speed of the target area is calculated according to the collection time, tracking area code and cell identifier in the target real-time vehicle data.

[0069] For example, after obtaining the target real-time vehicle data in a normal driving state, it is necessary to further filter the vehicle data that passes through several cells in the target area within T time to determine whether the vehicle is congested. It is necessary to obtain the speed and traffic volume of the vehicle within the unit area, and it is necessary to obtain the historical vehicle speed and traffic volume of the unit area as a reference standard. The specific algorithm is as follows:

[0070] Real-time traffic flow: According to the target real-time vehicle data, the number of vehicles in a specific tracking area is counted. Assuming that there are M vehicles in community A at the current moment, the real-time traffic flow of vehicles in community A is: F c =M;

[0071] Real-time speed: The real-time speed of the vehicle is calculated based on the cell identifier, tracking area code and collection time in the target real-time vehicle data. The average moving distance of the vehicle is calculated based on the real-time speed of the vehicle, so as to calculate the real-time speed of the vehicle. For example, assuming that there are M vehicles in cell A at the current moment, and the average moving distance of M vehicles from time T to now is S, the real-time speed of the vehicle in cell A is calculated as: V c =S / T;

[0072] Historical vehicle flow: Based on the historical vehicle data, the average number of vehicles in a specific tracking area in the historical time is counted. Assuming that the historical time T h In the case of M vehicles floating in the community A, the historical traffic volume of the vehicles in the community A is: h =M / T h ;

[0073] Historical speed: Calculate the historical speed of the vehicle based on the cell ID, tracking area code and collection time in the historical vehicle data. Assume that the historical time T h There are M vehicles in cell A, and the speed of the i-th vehicle is recorded as V i , then the historical speed of the vehicle in cell A is calculated as:

[0074] For example, in step S3, if the current real-time speed in the tracking area is lower than the historical speed and the real-time traffic volume exceeds the historical traffic volume, the area is considered to be congested and labeled as Traffic jam. The sliding window algorithm is used to track the trend of vehicle speed changes to further confirm the formation and development of potential congestion. Figure 3 As shown, Figure 3 It is a vehicle-machine congestion queue diagram provided by an embodiment of the present invention. A multi-level threshold system can be set. According to the queue conditions of the Traffic jam labels of adjacent cells, the congestion levels are divided into mild, moderate and severe, and marked with different colors to generate a vehicle-machine congestion queue diagram.

[0075] Furthermore, after generating the vehicle congestion heat map of the target area, the method further includes:

[0076] According to the vehicle congestion heat map, the traffic flow information of the target area is obtained to control the vehicle driving.

[0077] The embodiment of the present invention discloses a method for detecting the situation of an Internet of Vehicles, which obtains a GIS digital map of a target area and DPI network vehicle data of all vehicles in the target area; obtains basic vehicle information of the target area based on the DPI network vehicle data; generates a vehicle congestion queue diagram of the target area based on the basic vehicle information; generates a vehicle congestion heat map of the target area based on the vehicle congestion queue diagram and the GIS digital map. Based on the DPI real-time network vehicle data and the latitude and longitude information of the vehicle, there is no need for the car enterprise customer to provide other data source support. The area where the vehicle is located is calculated according to the physical site location reported by the vehicle and the cell signal coverage range, and the flow or number of vehicles in each area is obtained to draw a vehicle congestion heat map; at the same time, according to the reporting frequency of the vehicle and the mobile switching between cells, comparative heat maps of different time nodes can be obtained to show the dynamic changes of traffic flow.

[0078] See also Figure 4 , Figure 4 1 is a schematic diagram of the structure of a vehicle networking situation detection device 10 provided in an embodiment of the present invention. The vehicle networking situation detection device 10 includes:

[0079] The relevant data acquisition module 11 is used to obtain the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; wherein the DPI network vehicle data includes real-time vehicle data and historical vehicle data within a certain period of time; the real-time / historical vehicle data includes the collection time, IoT card ID, cell identification and tracking area code of all vehicles;

[0080] The vehicle information acquisition module 12 is used to obtain the vehicle basic information of the target area according to the DPI network vehicle data; wherein the vehicle basic information includes real-time vehicle flow, real-time speed, historical vehicle flow and historical speed;

[0081] A congestion queue map generating module 13, configured to generate a vehicle machine congestion queue map of the target area according to the vehicle machine basic information;

[0082] The congestion heat map generating module 14 is used to generate a vehicle congestion heat map of the target area according to the vehicle congestion queue map and the GIS digital map.

[0083] A vehicle networking situation detection device 10 provided in an embodiment of the present invention can implement all processes of the vehicle networking situation detection method of the above-mentioned embodiment. The functions of each module in the device and the technical effects achieved are respectively the same as the functions and technical effects achieved by the vehicle networking situation detection method of the above-mentioned embodiment, and will not be repeated here.

[0084] See also Figure 5 , Figure 5 : is a structural diagram of a vehicle networking situation detection device 20 provided in an embodiment of the present invention. The vehicle networking situation detection device 20 of this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above-mentioned vehicle networking situation detection method embodiment are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module in the above-mentioned vehicle networking situation detection device embodiment are implemented.

[0085] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the vehicle networking situation detection device 20.

[0086] The vehicle networking situation detection device 20 can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The vehicle networking situation detection device 20 can include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the vehicle networking situation detection device 20 and does not constitute a limitation on the vehicle networking situation detection device 20. It can include more or less components than shown in the figure, or combine certain components, or different components. For example, the vehicle networking situation detection device 20 can also include input and output devices, network access devices, buses, etc.

[0087] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the vehicle networking situation detection device 20, and uses various interfaces and lines to connect various parts of the entire vehicle networking situation detection device 20.

[0088] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the vehicle networking situation detection device 20 by running or executing the computer program and / or module stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0089] Wherein, if the module integrated in the vehicle networking situation detection device 20 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 21. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0090] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0091] An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the vehicle network situation detection method as described in the above embodiment.

[0092] In addition, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the vehicle network situation detection method of the above embodiment.

[0093] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for detecting situation of an Internet of Vehicles, characterized in that: include: Obtain the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; wherein the DPI network vehicle data includes real-time vehicle data and historical vehicle data within a certain period of time; the real-time / historical vehicle data includes the collection time, IoT card ID, cell identification and tracking area code of all vehicles; According to the DPI network vehicle data, the vehicle basic information of the target area is obtained; wherein the vehicle basic information includes real-time vehicle flow, real-time speed, historical vehicle flow and historical speed; Generate a vehicle-machine congestion queue diagram of the target area according to the vehicle-machine basic information; A vehicle-machine congestion heat map of the target area is generated according to the vehicle-machine congestion queue map and the GIS digital map.

2. The vehicle networking situation detection method according to claim 1, characterized in that: The step of generating a vehicle congestion heat map of the target area according to the vehicle congestion queue map and the GIS digital map includes: Calculating the road area and the ratio of the road area to the total area of ​​the target area according to the GIS digital map; If the ratio is greater than a preset ratio threshold, vehicles near the road network of the target area are converged into the road network according to the vehicle position information and speed of the vehicle congestion queue map, and a vehicle congestion heat map of the target area is generated.

3. The vehicle networking situation detection method according to claim 1, characterized in that: The obtaining of vehicle basic information of the target area according to the DPI network vehicle data includes: According to the real-time vehicle computer data, the vehicle computer status corresponding to all vehicles at the current moment is obtained; wherein the vehicle computer status includes normal driving, stopping, disconnection or no network; According to the vehicle computer state and the real-time vehicle computer data, target real-time vehicle computer data corresponding to the normal driving is obtained; The basic information of the vehicle computers in the target area is obtained according to the historical vehicle computer data and the target real-time vehicle computer data.

4. The vehicle networking situation detection method according to claim 3, characterized in that: The obtaining, according to the vehicle computer state and the real-time vehicle computer data, target real-time vehicle computer data corresponding to the normal driving includes: According to the real-time vehicle computer data and the preset driving and stopping conditions, obtaining the real-time vehicle computer data corresponding to the driving and stopping; According to the real-time vehicle computer data and the preset disconnection or no network condition, the real-time vehicle computer data corresponding to the disconnection or no network is obtained; The real-time vehicle machine data corresponding to the stopping and the disconnection or the no network are eliminated to obtain the target real-time vehicle machine data corresponding to the normal driving.

5. The vehicle networking situation detection method according to claim 3, characterized in that: The obtaining of vehicle computer basic information of the target area according to the historical vehicle computer data and the target real-time vehicle computer data includes: According to the historical vehicle data, the historical vehicle flow and speed of the target area are obtained; According to the target real-time vehicle machine data, the number of target vehicle machines in the target area is counted to obtain the real-time vehicle flow in the target area; The real-time speed of the target area is calculated according to the collection time, tracking area code and cell identifier in the target real-time vehicle data.

6. The vehicle networking situation detection method according to claim 1, characterized in that: After generating the vehicle congestion heat map of the target area, the method further includes: According to the vehicle congestion heat map, the traffic flow information of the target area is obtained to control the vehicle driving.

7. A vehicle networking situation detection device, characterized in that: include: The relevant data acquisition module is used to obtain the GIS digital map of the target area and the DPI network vehicle data of all vehicles in the target area; wherein the DPI network vehicle data includes real-time vehicle data and historical vehicle data within a certain period of time; the real-time / historical vehicle data includes the collection time, IoT card ID, cell identification and tracking area code of all vehicles; A vehicle information acquisition module, used to obtain the vehicle basic information of the target area according to the DPI network vehicle data; wherein the vehicle basic information includes real-time vehicle flow, real-time speed, historical vehicle flow and historical speed; A congestion queue map generating module, used for generating a vehicle machine congestion queue map of the target area according to the vehicle machine basic information; The congestion heat map generation module is used to generate a vehicle-machine congestion heat map of the target area according to the vehicle-machine congestion queue map and the GIS digital map.

8. A vehicle networking situation detection device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for detecting the situation of an Internet of Vehicles as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the vehicle network situation detection method as described in any one of claims 1-6.

10. A computer program product, characterized in that The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the vehicle network situation detection method as described in any one of claims 1-6.

Images