Intelligent Vehicle Congestion Management Method and Device Based on Cloud Computing Spatiotemporal Grid Division

Abstract

This application provides a vehicle congestion intelligent management method based on cloud computing spatiotemporal grid division. The method includes: acquiring real-time traffic flow information under different grids; detecting vehicle density in the real-time traffic flow information under different grids; and acquiring the current grid number; performing vehicle density detection based on vehicle travel direction on the highway under the current grid number; acquiring the latitude and longitude information of the highway under the current grid number; acquiring vehicle information under the latitude and longitude information of the highway under the current grid number; and intelligently managing vehicles when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane. This application improves the efficiency of intelligent congestion resolution on highways and saves vehicle travel time by acquiring information on vehicles causing congestion under specific latitude and longitude.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, specifically a method and processing device for intelligent vehicle congestion management and control based on cloud computing spatiotemporal grid division. Background Technology

[0002] A highway, or expressway for short, is a road specifically designed for high-speed automobile travel. The definition of a highway varies across different countries, regions, eras, and research fields. According to China's "Technical Standards for Highway Engineering" (JTGB01-2014), a highway is a multi-lane road with separate lanes for vehicular traffic and fully controlled access. The design speed for highways is 80 to 120 kilometers per hour.

[0003] However, the slower vehicles travel on highways, the greater the impact on traffic flow. On highways, some drivers tend to drive slowly in the fast lane, which is often occupied by medium and large passenger and freight vehicles. This significantly reduces the overall efficiency of the highway. Although many solutions exist for highway traffic congestion, research on congestion caused by slow-moving vehicles in the fast lane is relatively limited. Therefore, overcoming these technical problems and shortcomings is a key issue that needs to be addressed. Summary of the Invention

[0004] To overcome the traffic congestion problem caused by slow-moving vehicles in the fast lanes of highways, this application provides a vehicle congestion intelligent management method and device based on cloud computing spatiotemporal grid partitioning, employing the following technical solution:

[0005] Firstly, this application provides a method for intelligent vehicle congestion management based on cloud computing spatiotemporal grid partitioning, including:

[0006] Step S1: Based on the geographic information of the highway, the highway is divided into multiple grids using a preset spatiotemporal grid partitioning algorithm. Each grid represents a spatial region. The process of dividing the highway into multiple grids using the preset spatiotemporal grid partitioning algorithm includes numbering the multiple grids, with each grid having a unique identifier number.

[0007] Step S2: Obtain grid attribute information for each grid based on cloud computing. The grid attribute information for each grid includes vehicle density, vehicle speed, vehicle congestion status, and vehicle type for each grid.

[0008] Step S3: Detect the vehicle density of real-time traffic flow information under each grid, obtain the first vehicle density, and when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number.

[0009] Step S4: Based on the vehicle density of the highway under the current grid number, perform vehicle density detection according to the direction of vehicle travel to obtain a second vehicle density. When the second vehicle density is less than the preset vehicle density threshold, obtain the critical position between the first vehicle density and the second vehicle density. Based on the vehicle dynamic driving information of the critical position, obtain the dynamic latitude and longitude information of the critical position.

[0010] Step S5: Obtain vehicle information under the dynamic latitude and longitude information, including vehicle type, vehicle speed, and driving lane;

[0011] Step S6: When the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, intelligent management is performed on the vehicles causing congestion.

[0012] Furthermore, the highway geographic information in step S1 includes the highway segment length and intersection location.

[0013] Furthermore, the geographic information of the expressway is obtained through a geographic information data system.

[0014] Furthermore, the step S1 of dividing the highway into multiple grids using a preset spatiotemporal grid partitioning algorithm is specifically manifested as dividing the highway into multiple spatial grids using an equidistant partitioning method.

[0015] Furthermore, the acquisition of grid attribute information for each grid based on cloud computing in step S2 includes acquiring real-time traffic flow information through traffic monitoring cameras, ETC systems, and vehicle GPS.

[0016] Furthermore, the detection of vehicle density in the real-time traffic flow information of each grid in step S3 includes dynamically adjusting the adjacent grids when the vehicle density of adjacent grids is detected to be greater than a preset vehicle density. The method of dynamically adjusting the adjacent grids is to merge the adjacent grids.

[0017] Furthermore, the intelligent management of vehicles causing congestion described in step S6 includes:

[0018] (1) Obtain image information of the vehicle causing the congestion, locate and identify the license plate area of ​​the image information based on a preset image processing algorithm, and obtain the license plate number of the vehicle causing the congestion.

[0019] (2) Associate the obtained license plate number with the reminder module and the vehicle reminder module of the current grid;

[0020] (3) Activate the reminder module and vehicle reminder module of the current grid, and activate the warning module and vehicle reminder module of the current grid to remind the vehicle with the license plate number to change lanes.

[0021] Secondly, this application also provides a vehicle congestion intelligent management and control device based on cloud computing spatiotemporal grid partitioning, comprising:

[0022] The spatiotemporal grid division module is used to divide the highway into multiple grids based on the highway geographic information and using a preset spatiotemporal grid division algorithm. Each grid represents a spatial region. The process of dividing the highway into multiple grids using the preset spatiotemporal grid division algorithm includes numbering the multiple grids, with each grid having a unique identifier number.

[0023] The real-time traffic flow information acquisition module is used to acquire grid attribute information for each grid based on cloud computing. The grid attribute information includes vehicle density, vehicle speed, vehicle congestion status, and vehicle type for each grid.

[0024] The first vehicle density detection module is used to detect the vehicle density of real-time traffic flow information under each grid, obtain the first vehicle density, and when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number.

[0025] The second vehicle density detection module is used to detect vehicle density based on the vehicle density of the highway under the current grid number according to the direction of vehicle travel, and obtain the second vehicle density. When the second vehicle density is less than the preset vehicle density threshold, the module obtains the critical position between the first vehicle density and the second vehicle density, and obtains the dynamic latitude and longitude information of the critical position based on the vehicle dynamic driving information of the critical position.

[0026] The vehicle information acquisition module is used to acquire vehicle information under the dynamic latitude and longitude information, including vehicle type, vehicle speed, and driving lane.

[0027] The congestion intelligent management module is used to intelligently manage vehicles causing congestion when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane.

[0028] Thirdly, this application provides an electronic device, comprising:

[0029] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0030] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0031] Fifthly, this application provides a computer program that, when executed by a computer, performs the method described in the first aspect.

[0032] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor.

[0033] This application has the following beneficial effects:

[0034] 1. This application improves the efficiency of highway data processing and enables efficient management of highway traffic flow by numbering multiple grids, with each grid having a unique identifier.

[0035] 2. This application dynamically adjusts adjacent grids when the vehicle density in each adjacent grid is greater than a preset vehicle density, adapting to changes in traffic conditions in real time and efficiently managing vehicle flow.

[0036] 3. This application improves road utilization by intelligently managing vehicles that cause congestion, accurately identifying and resolving congestion problems. Attached Figure Description

[0037] Figure 1 This is an exemplary system architecture diagram to which embodiments of this application can be applied;

[0038] Figure 2 This is a flowchart illustrating the method of an embodiment of this application;

[0039] Figure 3 This is a schematic diagram of obtaining the first vehicle density according to an embodiment of this application;

[0040] Figure 4 This is a schematic diagram of obtaining the speed of a first vehicle according to an embodiment of this application;

[0041] Figure 5 This is a schematic diagram of an apparatus according to an embodiment of this application;

[0042] Figure 6 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0044] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0045] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0046] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0047] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0048] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0049] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0050] It should be noted that the intelligent vehicle congestion management method based on cloud computing spatiotemporal grid division provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the intelligent vehicle congestion management device based on cloud computing spatiotemporal grid division is generally installed in the server / terminal device.

[0051] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0052] Continue to refer to Figure 2 The figure shows a flowchart of a vehicle congestion intelligent management method based on cloud computing spatiotemporal grid partitioning according to this application. The method includes the following steps:

[0053] Step S1: Based on the geographic information of the highway, the highway is divided into multiple grids using a preset spatiotemporal grid partitioning algorithm. Each grid represents a spatial region. The process of dividing the highway into multiple grids using the preset spatiotemporal grid partitioning algorithm includes numbering the multiple grids, with each grid having a unique identifier.

[0054] In one possible implementation, the geographic information of the highway is obtained through a geographic information data system. The geographic information of the highway includes the length of the highway section and the location of the intersection. The highway is divided into multiple grids using a preset spatiotemporal grid partitioning algorithm. Specifically, the highway is divided into multiple spatial grids using an equidistant partitioning method.

[0055] Step S2: Obtain grid attribute information for each grid based on cloud computing. The grid attribute information for each grid includes vehicle density, vehicle speed, vehicle congestion status, and vehicle type for each grid.

[0056] In one possible implementation, the acquisition of grid attribute information for each grid based on cloud computing includes acquiring real-time traffic flow information through traffic monitoring cameras, ETC systems, and vehicle GPS.

[0057] Step S3: Detect the vehicle density of real-time traffic flow information under each grid, obtain the first vehicle density, and when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number.

[0058] In one possible implementation, please refer to the method for determining real-time traffic flow information under different grids. Figure 3 The specific steps are as follows:

[0059] Step 301a, Obtain first density information: Detect the vehicle density of real-time traffic flow information under different grids to obtain the first vehicle density.

[0060] Step 302a, determine the first density information: when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid.

[0061] Step 303a, Obtain the current grid number: Obtain the current grid number.

[0062] In one possible implementation, the method for determining real-time traffic flow information under different grids can also refer to... Figure 4 The specific steps are as follows:

[0063] Step 301b, Obtain first vehicle speed information: Detect the vehicle speed of the real-time traffic flow information under the different grids, and obtain the first vehicle speed.

[0064] Step 302b: Determine the speed information of the first vehicle: If the speed of the first vehicle is greater than the preset vehicle congestion threshold, then mark the current grid as a vehicle congestion grid.

[0065] Step 303b, Obtain the current grid number: Obtain the current grid number.

[0066] In one possible implementation, the detection of vehicle density in the real-time traffic flow information of each grid further includes dynamically adjusting the adjacent grids when the vehicle density of adjacent grids is detected to be greater than a preset vehicle density. The method of dynamically adjusting the adjacent grids is to merge the adjacent grids.

[0067] Step S4: Based on the vehicle density of the highway under the current grid number, perform vehicle density detection according to the direction of vehicle travel to obtain a second vehicle density. When the second vehicle density is less than the preset vehicle density threshold, obtain the critical position between the first vehicle density and the second vehicle density. Based on the vehicle dynamic driving information of the critical position, obtain the dynamic latitude and longitude information of the critical position.

[0068] In one possible implementation, the dynamic latitude and longitude information of the critical position is obtained based on the vehicle's dynamic driving information at the critical position. Figure 4 The corresponding method is as follows: based on the vehicle speed of the highway under the current grid number, the vehicle speed is detected according to the direction of vehicle travel to obtain a second vehicle speed. When the second vehicle speed is less than the preset vehicle speed threshold, the critical position between the first vehicle speed and the second vehicle speed is obtained. Based on the vehicle dynamic driving information of the critical position, the dynamic latitude and longitude information of the critical position is obtained.

[0069] Step S5: Obtain vehicle information under the dynamic latitude and longitude information, including vehicle type, vehicle speed, and driving lane;

[0070] In one possible implementation, when a slow-moving vehicle is traveling at high speed, its latitude and longitude information is dynamically changing. It is necessary to obtain the latitude and longitude information of the slow-moving vehicle in real time in order to accurately locate the vehicle.

[0071] In one possible implementation, the latitude and longitude information of a vehicle traveling in the wrong lane is dynamically changing, and it is necessary to obtain the latitude and longitude information of the vehicle traveling in the wrong lane in real time in order to accurately locate the vehicle traveling in the wrong lane.

[0072] Step S6: When the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, intelligent management is performed on the vehicles causing congestion.

[0073] In one possible implementation, the intelligent management of vehicles causing congestion includes:

[0074] (1) Obtain image information of the vehicle causing the congestion, locate and identify the license plate area of ​​the image information based on a preset image processing algorithm, and obtain the license plate number of the vehicle causing the congestion.

[0075] (2) Associate the obtained license plate number with the reminder module and the vehicle reminder module of the current grid;

[0076] (3) Activate the reminder module and vehicle reminder module of the current grid, and activate the warning module and vehicle reminder module of the current grid to remind the vehicle with the license plate number to change lanes.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0078] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0079] Continue to refer to Figure 5 The intelligent vehicle congestion management and control device based on cloud computing spatiotemporal grid partitioning described in this embodiment includes:

[0080] The spatiotemporal grid division module 501 is used to divide the highway into multiple grids based on the highway geographic information and using a preset spatiotemporal grid division algorithm. Each grid represents a spatial region. The process of dividing the highway into multiple grids using the preset spatiotemporal grid division algorithm includes numbering the multiple grids, with each grid having a unique identifier number.

[0081] The real-time traffic flow information acquisition module 502 is used to acquire grid attribute information for each grid based on cloud computing. The grid attribute information includes vehicle density, vehicle speed, vehicle congestion status, and vehicle type for each grid.

[0082] The first vehicle density detection module 503 is used to detect the vehicle density of real-time traffic flow information under each grid, obtain the first vehicle density, and when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number.

[0083] The second vehicle density detection module 504 is used to detect vehicle density based on the vehicle density of the highway under the current grid number according to the direction of vehicle travel, obtain the second vehicle density, and when the second vehicle density is less than the preset vehicle density threshold, obtain the critical position between the first vehicle density and the second vehicle density, and obtain the dynamic latitude and longitude information of the critical position based on the vehicle dynamic driving information of the critical position.

[0084] The vehicle information acquisition module 505 is used to acquire vehicle information under the dynamic latitude and longitude information, the vehicle information including vehicle type, vehicle speed, and driving lane.

[0085] The congestion intelligent management module 506 is used to intelligently manage vehicles causing congestion when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane.

[0086] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0087] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected via a system bus. It should be noted that only the computer device 6 with components 6a-6c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0088] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0089] The memory 6a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 6a may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 6a may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 6a may also include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 6a is typically used to store the operating system and various application software installed on the computer device 6, such as the program code of a vehicle congestion intelligent management method and processing device based on cloud computing spatiotemporal grid partitioning. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or will be output.

[0090] In some embodiments, the processor 6b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 6b is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run program code stored in the memory 6a or process data, for example, to run the program code of the intelligent vehicle congestion management method and processing device based on cloud computing spatiotemporal grid partitioning.

[0091] The network interface 6c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0092] This application also provides another embodiment, namely, a non-volatile computer-readable storage medium storing a program of a vehicle congestion intelligent management method and processing device based on cloud computing spatiotemporal grid partitioning. The vehicle congestion intelligent management method and processing device based on cloud computing spatiotemporal grid partitioning can be executed by at least one processor to enable the at least one processor to perform the steps of the vehicle congestion intelligent management method and processing device based on cloud computing spatiotemporal grid partitioning as described above.

[0093] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0094] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for intelligent vehicle congestion management based on cloud computing spatiotemporal grid partitioning, characterized in that, include: Step S1: Based on the geographic information of the highway, the highway is divided into multiple grids using a preset spatiotemporal grid partitioning algorithm. Each grid represents a spatial region. The process of dividing the highway into multiple grids using the preset spatiotemporal grid partitioning algorithm includes numbering the multiple grids, with each grid having a unique identifier number. Step S2: Obtain grid attribute information for each grid based on cloud computing. The grid attribute information for each grid includes vehicle density, vehicle speed, vehicle congestion status, and vehicle type for each grid. Step S3: Detect the vehicle density of real-time traffic flow information under each grid, obtain the first vehicle density, and when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number; when it is detected that the vehicle density of adjacent grids is greater than the preset vehicle density, merge the adjacent grids. Step S4: Based on the vehicle density of the highway under the current grid number, perform vehicle density detection according to the direction of vehicle travel to obtain a second vehicle density. When the second vehicle density is less than the preset vehicle density threshold, obtain the critical position between the first vehicle density and the second vehicle density. Based on the vehicle dynamic driving information of the critical position, obtain the dynamic latitude and longitude information of the critical position. Step S5: Obtain vehicle information under the dynamic latitude and longitude information, including vehicle type, vehicle speed, and driving lane; Step S6: When the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, intelligent management is performed on the vehicles causing congestion.

2. The intelligent vehicle congestion management method based on cloud computing spatiotemporal grid partitioning according to claim 1, characterized in that, The highway geographic information in step S1 includes the length of the highway section and the location of the intersection.

3. The intelligent vehicle congestion management method based on cloud computing spatiotemporal grid partitioning according to claim 1, characterized in that, The acquisition of grid attribute information for each grid based on cloud computing in step S2 includes acquiring real-time traffic flow information through traffic monitoring cameras, ETC systems, and vehicle GPS.

4. The intelligent vehicle congestion management method based on cloud computing spatiotemporal grid partitioning according to claim 1, characterized in that, The intelligent management and control of vehicles causing congestion described in step S6 includes: (1) Obtain image information of the vehicle causing the congestion, locate and identify the license plate area of ​​the image information based on a preset image processing algorithm, and obtain the license plate number of the vehicle causing the congestion; (2) Associate the obtained license plate number with the reminder module and the vehicle reminder module of the current grid; (3) Activate the reminder module and vehicle reminder module of the current grid, and activate the warning module and vehicle reminder module of the current grid to remind the vehicle with the license plate number to change lanes.

5. A vehicle congestion intelligent management and control device based on cloud computing spatiotemporal grid partitioning, characterized in that, include: The spatiotemporal grid division module is used to divide the highway into multiple grids based on the highway geographic information and using a preset spatiotemporal grid division algorithm. Each grid represents a spatial region. The process of dividing the highway into multiple grids using the preset spatiotemporal grid division algorithm includes numbering the multiple grids, with each grid having a unique identifier number. The real-time traffic flow information acquisition module is used to acquire grid attribute information for each grid based on cloud computing. The grid attribute information includes vehicle density, vehicle speed, vehicle congestion status, and vehicle type for each grid. The first vehicle density detection module is used to detect the vehicle density of real-time traffic flow information under each grid, obtain the first vehicle density, and when the first vehicle density is greater than the preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number; when it is detected that the vehicle density of adjacent grids is greater than the preset vehicle density, the adjacent grids are merged. The second vehicle density detection module is used to detect vehicle density based on the vehicle density of the highway under the current grid number according to the direction of vehicle travel, and obtain the second vehicle density. When the second vehicle density is less than the preset vehicle density threshold, the module obtains the critical position between the first vehicle density and the second vehicle density, and obtains the dynamic latitude and longitude information of the critical position based on the vehicle dynamic driving information of the critical position. The vehicle information acquisition module is used to acquire vehicle information under the dynamic latitude and longitude information, including vehicle type, vehicle speed, and driving lane. The congestion intelligent management module is used to intelligently manage vehicles causing congestion when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane.

6. An electronic device, characterized in that, include: One or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the intelligent vehicle congestion management method based on cloud computing spatiotemporal grid partitioning as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to execute the intelligent vehicle congestion management method based on cloud computing spatiotemporal grid partitioning as described in any one of claims 1 to 4.

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